Pith. sign in

REVIEW 2 major objections 5 minor 2 cited by

VectorLLM: Human-like Extraction of Structured Building Contours vis Multimodal LLMs

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims a multimodal LLM that outputs building contours corner-point by corner-point beats multi-stage segmentation-vectorization pipelines by 5.6-13.6 AP and zero-shots to unseen objects.

desk verdict A genuinely new MLLM-based approach to building contour extraction, but the headline AP gains are inflated by an unfair crop protocol and the zero-shot claim outruns the evidence. read the letter →

arxiv 2507.04664 v1 pith:M3MABDGB submitted 2025-07-07 cs.CV

Tao Zhang , Shiqing Wei , Shihao Chen , Wenling Yu , Muying Luo , Shunping Ji This is my paper
classification cs.CV
keywords buildingcontourextractionmultimodallargelanguagemodelremotesensingimagerycorner-pointregressionzero-shotgeneralizationpreferenceoptimizationvectorization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a multimodal large language model, fed a single cropped building image, can write out the building's vector outline as a sequence of corner-point coordinates, the way a human annotator might, and that this direct approach outperforms the standard multi-stage pipeline of pixel segmentation, vectorization, and polygon refinement. The proposed system, VectorLLM, is trained in three stages: pretraining, supervised fine-tuning, and preference optimization, and a single set of weights beats previous state-of-the-art methods by 5.6 AP, 7.1 AP, and 13.6 AP on the WHU, WHU-Mix, and CrowdAI datasets. The paper further reports that the same model draws plausible contours for objects it never trained on, such as aircraft, water bodies, and oil tanks. If these results hold, vector extraction from remote sensing imagery would become a single learned step rather than a fragile assembly of separate algorithms, possibly extending to roads, water, and other geographic features.

What carries the argument

The load-bearing mechanism is corner-by-corner autoregressive decoding into a coordinate vocabulary: the LLM emits one special token per x- or y-value, which forces the model to commit to a spatial position at each step and lets it use the already-emitted corners as context, exactly like a human tracing a polygon. Three training stages carry the argument: pretraining aligns vision features to the LLM with plain coordinate-string targets, supervised fine-tuning imposes a fixed orientation and starting point (top-left, clockwise) so outputs are regular and stable, and direct preference optimization (DPO) on automatically constructed chosen/rejected pairs cleans up repeated outputs, missing small lines, and roof-plane interference. An upstream FCOS detector with a Swin-L backbone crops each building instance, with boxes enlarged 1.3x, so the model sees one prominent building per image. The 128x128 input resolution and one-token-per-coordinate formulation keep the design deliberately simple.

What would settle it

Run P2PFormer, Line2Poly, and BuildMapper under the identical protocol—FCOS/Swin-L detections, 1.3x enlarged crops, single-building inputs—and compare AP on WHU, WHU-Mix, and CrowdAI; if the margins shrink to near zero, the central claim of LLM superiority for contour extraction does not hold. A complementary check would be to feed VectorLLM full scene images without any detector crops and measure the resulting drop, which would quantify how much of the system's capability depends on the external detector.

Watch

Extended reading notes

Core claim

VectorLLM recasts regular building contour extraction as an autoregressive language modeling task: the model predicts the next corner point's x- and y-coordinates, expressed as special tokens, conditioned on the image and the previously emitted corners. The architecture attaches a RADIO vision encoder to a Qwen3 LLM through an MLP projector, adds learnable position embeddings to the vision tokens, and trains with next-token prediction on coordinate strings, then on instruction-style data, then with direct preference optimization on chosen versus rejected contours. On the three benchmark datasets the model reports 79.4 AP, 68.4/56.4 AP (in-domain/out-of-domain), and 79.6 AP with detector-supplied boxes, and 90.2, 78.5/64.6, and 87.7 AP with oracle boxes, exceeding prior methods across the board. The authors argue this demonstrates that LLM-based topological reasoning is a qualitative leap for vector extraction, not an incremental gain.

Load-bearing premise

The headline gains assume the comparison protocol is fair: VectorLLM is evaluated on single-building crops produced by a separately trained detector, while the prior methods it beats are evaluated in their native end-to-end setting on full images, and the paper does not report those baselines under the same crop-and-recall protocol.

Editorial extensions

If this is right

  • Building contour extraction collapses from three pipeline stages to one trained model: no pixel segmentation, no separate vectorization, and no polygon refinement step.
  • A single co-trained model with one set of weights transfers across datasets with different annotation standards and image styles, as shown by the out-of-domain WHU-Mix test-2 score.
  • Zero-shot contours for aircraft, water bodies, oil tanks, roads, and general objects imply the corner-point skill generalizes beyond buildings, opening a route to unified vector extraction for multiple geographic feature types.
  • Scaling the LLM from 0.6B to 1.7B parameters improves AP on every dataset, so further scaling is expected to yield more gains.
  • Preference optimization contributes a measurable ~1.8 AP gain and specifically fixes corner cases like duplicated nodes and missed small edges, so alignment techniques transfer to geometric outputs.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The reported gains compare detector-cropped VectorLLM against prior methods evaluated end-to-end on full images; with the same crop protocol, the baselines might narrow the gap, so the cleanest attribution of the improvement to the LLM itself requires a matched evaluation.
  • Because VectorLLM cannot yet find the buildings it delineates, the system's end-to-end accuracy is capped by the external detector; making the LLM itself localize instances would be the natural next step toward a true single-model pipeline.
  • The fixed 128x128 input resolution is the likely cause of the reported failures on large and complex buildings; a multi-resolution or patch-based visual front end could raise the ceiling on fine detail.
  • The zero-shot success on objects with very different geometry suggests the model may have learned a general straight-line-polygon prior during building training; testing on non-polygonal or curved boundaries, such as meandering rivers, would show how general that prior really is.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. VectorLLM introduces a multimodal large language model that extracts regular building contours by autoregressively predicting corner-point coordinates from single-building image crops. The architecture combines a RADIO vision encoder, learnable positional embeddings, an MLP projector, and a Qwen3-based language model, with coordinate-specific tokens added to the vocabulary. Training proceeds through pretraining, supervised fine-tuning, and direct preference optimization, using the WHU, WHU-Mix, CrowdAI, and COCO datasets. The paper reports state-of-the-art AP on the three building datasets and shows qualitative zero-shot contour extraction on aircraft, ships, water bodies, and other objects. The work also includes ablations of the positional embedding, vision encoder, training stages, model size, coordinate tokenization, and data scaling.

Significance. If the reported comparisons were controlled, this would be a significant result: a detector-plus-autoregressive-LLM system could replace multi-stage segmentation, vectorization, and refinement pipelines, and the oracle-box results (90.2 AP on WHU) suggest that the contour regression component itself is highly accurate. The ablations are systematic and the DPO preference-data construction is standard. The main weakness is that the headline SOTA margins compare VectorLLM under a detector-crop protocol to baselines evaluated end-to-end on full images, so the margins cannot yet be cleanly attributed to the LLM or the point-by-point task formulation. The zero-shot claim is also weakened by the inclusion of COCO in pretraining. A controlled comparison under the same protocol is needed before the central claim is established.

major comments (2)
  1. [§3.5, §4.1, Tables 1–3] The headline SOTA margins are not based on a controlled comparison. In §3.5 and §4.1, VectorLLM is tested on single-building crops produced by an external FCOS/Swin-L detector with 1.3× box enlargement, while prior methods such as P2PFormer, Line2Poly, and BuildMapper are evaluated end-to-end from full images. The paper does not report the detector's recall or run any baseline on the same detector-crop protocol, so the 5.6, 7.1, and 13.6 AP margins in Tables 1–3 may reflect the external detector's localization and instance separation rather than the LLM's contour quality. The oracle-bbox row in Table 1 (90.2 vs. 79.4 AP) further shows that bounding-box quality alone changes AP by about 11 points, so detector effects are material. The revision should report AP for P2PFormer, Line2Poly, and BuildMapper on the same FCOS crops, and should also report detector recall, before claiming that the LLM formulation is responsible for the gains.
  2. [§4.1, §4.4, Figure 5] The zero-shot claim is overstated because the model is pretrained on COCO. Section 4.1 states that VectorLLM is jointly trained on WHU, WHU-Mix, CrowdAI, and COCO training datasets, and Section 4.4 describes the results on aircraft, ships, water bodies, and oil tanks as objects that 'never appear in the training set.' However, COCO contains airplanes and ships, so those categories are not unseen if COCO instance masks are converted into contour sequences under the pretraining protocol described in §3.2. The paper should either exclude COCO categories from the zero-shot evaluation, report which COCO categories are actually used, or qualify the 'unseen' claim to categories absent from all training data.
minor comments (5)
  1. [Title/Abstract] The title contains a typo ('vis' should be 'via'), and the abstract's 'significantly outperformed' should be rephrased as 'outperforms' or 'outperformed' with a clear subject.
  2. [Table 7] The column headers for WHU, CrowdAI, and WHU-Mix do not indicate the metric; 'AP' should be included in each numeric column or specified in the caption.
  3. [Figures 1, 5, and 6] The captions state that the black bounding boxes are manually provided by users, but Section 4.1 describes a detector-based test protocol; the paper should clarify how the manual-box visualizations relate to the quantitative FCOS-based results and whether the qualitative figures use manual or detector boxes.
  4. [§3.4] The DPO data-generation rule 'IoU less than 0.8' should specify whether the IoU is computed between the predicted polygon and the ground-truth polygon, and in which coordinate space (image pixels or normalized coordinates).
  5. [§4.3, Table 6] The check-mark rows in the tuning-strategy ablation are not self-explanatory; a row label such as 'Pretraining only' would help readers interpret the effect of each training stage.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the central results rest on public benchmarks and independent baselines, and the protocol concerns raised are fairness issues, not circular reductions.

full rationale

The paper's derivation chain is self-contained against external evidence. VectorLLM is trained on public building datasets (WHU, WHU-Mix, CrowdAI) plus COCO and evaluated with standard AP metrics against published baselines; the headline margins therefore do not reduce by construction to the model's own training labels. The DPO stage (§3.4) uses the SFT model's own low-IoU outputs as rejected answers and labels as chosen answers, but this is a standard preference-optimization construction and evaluation is performed on held-out test sets, so it is not a fitted input being renamed as a prediction. The paper's self-citations (BuildMapper, P2PFormer, Line2Poly) are prior published methods used as comparison baselines, not as load-bearing justifications for the present claims, and no uniqueness theorem or ansatz is imported from the authors' own prior work. The bounding-box cropping protocol (§3.5, §4.1) raises a legitimate comparison-fairness concern because prior methods are evaluated end-to-end on full images, and the COCO pretraining in §4.1 weakens the zero-shot claim for objects that appear in COCO; however, these are experimental-design limitations, not cases where an output is equivalent to an input by definition or where a derivation is forced by self-citation. No circular step can be exhibited from the paper's own equations or citations, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims rest on a small set of hand-chosen hyperparameters and several domain assumptions about the task framing. No invented physical or conceptual entities are introduced. The largest unstated premise is that the comparison protocol isolates the LLM's contribution from the detector's.

free parameters (4)
  • Input resolution 128x128 = 128x128 (hand-chosen)
    The coordinate vocabulary is bounded by this resolution, and Section 4.5 admits large-building edge bias and detail loss as a consequence of this choice.
  • Test cropping enlargement factor = 1.3x
    Section 4.1 applies a 1.3x enlargement to every bounding box before cropping; this value is chosen by hand and is not ablated.
  • DPO KL penalty beta = 0.5
    Section 3.4 sets beta to 0.5 in the DPO loss without reporting an ablation over this value.
  • DPO IoU threshold = 0.8
    Section 3.4 uses predictions with IoU less than 0.8 as rejected answers; this threshold is hand-chosen and not ablated.
assumptions (5)
  • domain assumption Building contours can be represented as a single closed polygon without holes.
    Section 4.5 admits that VectorLLM cannot handle hollow buildings or objects segmented into multiple parts, so the method only targets simple regular buildings.
  • domain assumption A single prominent building per crop is a sufficient unit for the task.
    Section 3.5 restricts inference to one instance per crop because LLMs cannot directly handle many instances in one image; this shapes the entire evaluation protocol.
  • domain assumption The LLM's pretrained reasoning is the cause of the performance gain.
    The paper's central thesis in Sections 1 and 5.1 is that LLM reasoning produces the gains, but no controlled experiment isolates the LLM from the external detector and the crop protocol.
  • domain assumption Objects shown as zero-shot are absent from all training data.
    Section 4.1 includes COCO in pretraining; COCO contains airplane and boat categories, so the zero-shot claim in Section 4.4 is violated for those categories.
  • ad hoc to paper COCO instance masks can be converted into contour sequences and used as pretraining data without changing the building-focused objective.
    The authors add COCO to pretraining (Section 4.1, Table 9) but do not describe how the masks are polygonized or how this affects the model's notion of buildings.

how reviews work

0 comments
Cite this review

Pith. "Pith review of VectorLLM: Human-like Extraction of Structured Building Contours vis Multimodal LLMs." pith.science (2026). https://pith.science/paper/M3MABDGB

@misc{pith2026250704664,
  author       = {Pith},
  title        = {Pith review of: VectorLLM: Human-like Extraction of Structured Building Contours vis Multimodal LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M3MABDGB}},
  note         = {Machine review of arXiv:2507.04664}
}
read the original abstract

Automatically extracting vectorized building contours from remote sensing imagery is crucial for urban planning, population estimation, and disaster assessment. Current state-of-the-art methods rely on complex multi-stage pipelines involving pixel segmentation, vectorization, and polygon refinement, which limits their scalability and real-world applicability. Inspired by the remarkable reasoning capabilities of Large Language Models (LLMs), we introduce VectorLLM, the first Multi-modal Large Language Model (MLLM) designed for regular building contour extraction from remote sensing images. Unlike existing approaches, VectorLLM performs corner-point by corner-point regression of building contours directly, mimicking human annotators' labeling process. Our architecture consists of a vision foundation backbone, an MLP connector, and an LLM, enhanced with learnable position embeddings to improve spatial understanding capability. Through comprehensive exploration of training strategies including pretraining, supervised fine-tuning, and preference optimization across WHU, WHU-Mix, and CrowdAI datasets, VectorLLM significantly outperformed the previous SOTA methods by 5.6 AP, 7.1 AP, 13.6 AP, respectively in the three datasets. Remarkably, VectorLLM exhibits strong zero-shot performance on unseen objects including aircraft, water bodies, and oil tanks, highlighting its potential for unified modeling of diverse remote sensing object contour extraction tasks. Overall, this work establishes a new paradigm for vector extraction in remote sensing, leveraging the topological reasoning capabilities of LLMs to achieve both high accuracy and exceptional generalization. All the codes and weights will be published for promoting community development.

Figures

Figures reproduced from arXiv: 2507.04664 by the authors.

Figure 1
Figure 1. The strong capability of VectorLLM. The black bounding boxes are manually provided by users. The subfigures within the green and cyan borders in the first row demonstrate that VectorLLM can accurately extract regular contours of buildings without any post-processing processes. The subfigures within the yellow borders show that VectorLLM can flexibly handle multi-granularity vector building contour extraction tasks, … view at source ↗
Figure 2
Figure 2. The architecture of VectorLLM. VectorLLM has a streamlined architecture, consisting of a vision encoder, learnable positional embedding, MLP projector, and LLM. VectorLLM will generate regular building contours point by point in an auto-regressive manner. Large Language Model Vision Encoder Projector Position Embeddings Image Text Instruction Large Language Model Vision Encoder Projector Position Embeddings Image Te… view at source ↗
Figure 3
Figure 3. Training stages of VectorLLM. The training of VectorLLM consists of pretraining, supervised finetuning, and preference optimization stages. the CrowdAI dataset, similarly surpassing current SOTA mod￾els in performance. It’s worth noting that VectorLLM is co￾trained on three building datasets and tested using the same weights, without separate fine-tuning on any specific dataset. Finally, we are pleasantly surprised … view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: The visualization results on CrowdAI dataset. Although the low image resolution and low brightness of the CrowdAI dataset limit VectorLLM’s performance, VectorLLM still demonstrates more accurate and stable topology prediction results compared to PolyWorld and P2PForme…
Figure 5
Figure 5. Figure 5: The visualization results of VectorLLM’s zero-shot prediction results. VectorLLM demonstrates strong zero-shot performance on feature types that do not exist in the training set, such as roads, water bodies, vegetation, and playgrounds. The black bounding boxes in the …
Figure 6
Figure 6. Figure 6: The prediction results of VectorLLM on general objects. All images come from SAM [Kirillov et al., 2023a]. VectorLLM possesses strong vector contour extraction capabilities for multi-granularity general objects. Each image following the image with multiple bounding box…
Figure 7
Figure 7. Figure 7: The visualization results on WHU and WHU-Mix datasets. VectorLLM demonstrates more stable and accurate vector extraction results compared to BuildMapper. data, VectorLLM’s performance continues to improve, even though there are significant differences in domain, annota…
Figure 8
Figure 8. Figure 8: Visualization results of failure cases. 5. DISCUSSION 5.1 LLMs vs. conventional deep methods As shown in Tab. 1, 2, and 3, VectorLLM significantly outper￾forms SOTA methods by 5.6 AP, 7.1 AP, and 13.6 AP on the WHU, WHU-Mix and CrowdAI datasets, respectively. Taking th…

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Actor as Its Own Critic: Unifying Region Understanding and Localization via CycleGRPO

    cs.CV 2026-07 accept novelty 7.0 of 10

    A single MLLM jointly improves region captioning and localization by rewarding captions that let it reconstruct the original mask, needing only region inputs.

  2. HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery

    cs.CV 2025-07 conditional novelty 6.0 of 10

    HoliTracer is a framework that directly vectorizes geographic objects from very high resolution images over 10,000 pixels wide, using multi-scale attention and contour-sequence tracing.

Reference graph

Works this paper leans on

106 extracted references · 53 canonical work pages · cited by 2 Pith papers

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION list.label.output make.list.label ", " * write FUNCTION article output.bibitem list.label.output list.year output new.block title output new.block journal emphasize output format.volume.number output pages output new.block url output new.block note output fin.entry FUNCTION book output.bibitem list.l...

  2. [2]

    Efficient interactive annotation of segmentation datasets with polygon-rnn++

    Acuna, D., Ling, H., Kar, A., Fidler, S., 2018. Efficient interactive annotation of segmentation datasets with polygon-rnn++. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 859--868

    2018

  3. [3]

    et al., 2023a

    Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F. et al., 2023a. Qwen technical report. arXiv preprint arXiv:2309.16609

    arXiv

  4. [4]

    Qwen-vl: A frontier large vision-language model with versatile abilities

    Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., Zhou, J., 2023b. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966

    arXiv

  5. [5]

    Multi-task learning for segmentation of building footprints with deep neural networks

    Bischke, B., Helber, P., Folz, J., Borth, D., Dengel, A., 2019. Multi-task learning for segmentation of building footprints with deep neural networks. 2019 IEEE International Conference on Image Processing (ICIP), IEEE, 1480--1484

    2019

  6. [6]

    J., 2019

    Bolya, D., Zhou, C., Xiao, F., Lee, Y. J., 2019. Yolact: Real-time instance segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision, 9157--9166

    2019

  7. [7]

    et al., 2024

    Cai, Z., Cao, M., Chen, H., Chen, K., Chen, K., Chen, X., Chen, X., Chen, Z., Chen, Z., Chu, P. et al., 2024. Internlm2 technical report. arXiv preprint arXiv:2403.17297

    arXiv 2024

  8. [8]

    Annotating object instances with a polygon-rnn

    Castrejon, L., Kundu, K., Urtasun, R., Fidler, S., 2017. Annotating object instances with a polygon-rnn. Proceedings of the IEEE conference on computer vision and pattern recognition, 5230--5238

    2017

Show all 106 references
  1. [9]

    ASF-Net: Adaptive Screening Feature Network for Building Footprint Extraction From Remote-Sensing Images

    Chen, J., Jiang, Y., Luo, L., Gong, W., 2022. ASF-Net: Adaptive Screening Feature Network for Building Footprint Extraction From Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing , 60, 1--13

    2022

  2. [10]

    RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model

    Chen, K., Liu, C., Chen, H., Zhang, H., Li, W., Zou, Z., Shi, Z., 2024a. RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model. IEEE Transactions on Geoscience and Remote Sensing

  3. [11]

    L., Liu, X., 2020

    Chen, Q., Wang, L., Waslander, S. L., Liu, X., 2020. An end-to-end shape modeling framework for vectorized building outline generation from aerial images. ISPRS Journal of Photogrammetry and Remote Sensing , 170, 114--126

    2020

  4. [12]

    CGSANet: A Contour-Guided and Local Structure-Aware Encoder--Decoder Network for Accurate Building Extraction From Very High-Resolution Remote Sensing Imagery

    Chen, S., Shi, W., Zhou, M., Zhang, M., Xuan, Z., 2021. CGSANet: A Contour-Guided and Local Structure-Aware Encoder--Decoder Network for Accurate Building Extraction From Very High-Resolution Remote Sensing Imagery. IEEE Journal of Selected Topics in Applied Earth Observations...

    2021

  5. [13]

    et al., 2024b

    Chen, Z., Wang, W., Cao, Y., Liu, Y., Gao, Z., Cui, E., Zhu, J., Ye, S., Tian, H., Liu, Z. et al., 2024b. Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling. arXiv preprint arXiv:2412.05271

    arXiv

  6. [14]

    et al., 2024c

    Chen, Z., Wang, W., Tian, H., Ye, S., Gao, Z., Cui, E., Tong, W., Hu, K., Luo, J., Ma, Z. et al., 2024c. How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites. arXiv preprint arXiv:2404.16821

    arXiv

  7. [15]

    et al., 2024d

    Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L. et al., 2024d. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re...

  8. [16]

    G., Kirillov, A., Girdhar, R., 2022

    Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., Girdhar, R., 2022. Masked-attention mask transformer for universal image segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1290--1299

    2022

  9. [17]

    E., Stoica, I., Xing, E

    Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., Xing, E. P., 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90\

    2023

  10. [18]

    et al., 2020

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. et al., 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

    2020 arXiv

  11. [19]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR

    2021

  12. [20]

    et al., 2024

    Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A. et al., 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783

    2024 arXiv

  13. [21]

    Geollava: Efficient fine-tuned vision-language models for temporal change detection in remote sensing

    Elgendy, H., Sharshar, A., Aboeitta, A., Ashraf, Y., Guizani, M., 2024. Geollava: Efficient fine-tuned vision-language models for temporal change detection in remote sensing. arXiv preprint arXiv:2410.19552

    2024 arXiv

  14. [22]

    Instances as queries

    Fang, Y., Yang, S., Wang, X., Li, Y., Fang, C., Shan, Y., Feng, B., Liu, W., 2021. Instances as queries. Proceedings of the IEEE/CVF International Conference on Computer Vision, 6910--6919

    2021

  15. [23]

    GPT-3: Its nature, scope, limits, and consequences

    Floridi, L., Chiriatti, M., 2020. GPT-3: Its nature, scope, limits, and consequences. Minds and Machines , 30, 681--694

    2020

  16. [24]

    Polygonal building extraction by frame field learning

    Girard, N., Smirnov, D., Solomon, J., Tarabalka, Y., 2021. Polygonal building extraction by frame field learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5891--5900

    2021

  17. [25]

    Remote sensing chatgpt: Solving remote sensing tasks with chatgpt and visual models

    Guo, H., Su, X., Wu, C., Du, B., Zhang, L., Li, D., 2024. Remote sensing chatgpt: Solving remote sensing tasks with chatgpt and visual models. arXiv preprint arXiv:2401.09083

    2024 arXiv

  18. [26]

    HigherNet-DST: Higher resolution network with dynamic scale training for rooftop delineation

    He, H., Ma, L., Li, J., 2024. HigherNet-DST: Higher resolution network with dynamic scale training for rooftop delineation. IEEE Transactions on Geoscience and Remote Sensing

    2024

  19. [27]

    Mask r-cnn

    He, K., Gkioxari, G., Doll \'a r, P., Girshick, R., 2017. Mask r-cnn. Proceedings of the IEEE international conference on computer vision, 2961--2969

    2017

  20. [28]

    Rsgpt: A remote sensing vision language model and benchmark

    Hu, Y., Yuan, J., Wen, C., Lu, X., Liu, Y., Li, X., 2025. Rsgpt: A remote sensing vision language model and benchmark. ISPRS Journal of Photogrammetry and Remote Sensing , 224, 272--286

    2025

  21. [29]

    Sequentially delineation of rooftops with holes from VHR aerial images using a convolutional recurrent neural network

    Huang, W., Liu, Z., Tang, H., Ge, J., 2021a. Sequentially delineation of rooftops with holes from VHR aerial images using a convolutional recurrent neural network. Remote Sensing , 13(21), 4271

  22. [30]

    OEC-RNN: Object-oriented delineation of rooftops with edges and corners using the recurrent neural network from the aerial images

    Huang, W., Tang, H., Xu, P., 2021b. OEC-RNN: Object-oriented delineation of rooftops with edges and corners using the recurrent neural network from the aerial images. IEEE Transactions on Geoscience and Remote Sensing , 60, 1--12

  23. [31]

    A., Liu, E

    Irvin, J. A., Liu, E. R., Chen, J. C., Dormoy, I., Kim, J., Khanna, S., Zheng, Z., Ermon, S., 2024. Teochat: A large vision-language assistant for temporal earth observation data. arXiv preprint arXiv:2410.06234

    2024 arXiv

  24. [32]

    Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set

    Ji, S., Wei, S., Lu, M., 2018. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Transactions on geoscience and remote sensing , 57(1), 574--586

    2018

  25. [33]

    A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery

    Ji, S., Wei, S., Lu, M., 2019. A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery. International journal of remote sensing , 40(9), 3308--3322

    2019

  26. [34]

    C., Lo, W.-Y

    Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y. et al., 2023a. Segment anything. arXiv preprint arXiv:2304.02643

    arXiv

  27. [35]

    C., Lo, W.-Y

    Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y. et al., 2023b. Segment anything. Proceedings of the IEEE/CVF International Conference on Computer Vision, 4015--4026

  28. [36]

    E., 2017

    Krizhevsky, A., Sutskever, I., Hinton, G. E., 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM , 60(6), 84--90

    2017

  29. [37]

    S., Naseer, M., Das, A., Khan, S., Khan, F

    Kuckreja, K., Danish, M. S., Naseer, M., Das, A., Khan, S., Khan, F. S., 2024. Geochat: Grounded large vision-language model for remote sensing. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 27831--27840

    2024

  30. [38]

    Lisa: Reasoning segmentation via large language model

    Lai, X., Tian, Z., Chen, Y., Li, Y., Yuan, Y., Liu, S., Jia, J., 2024. Lisa: Reasoning segmentation via large language model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9579--9589

    2024

  31. [39]

    VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding

    Li, X., Ding, J., Elhoseiny, M., 2024. VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. arXiv preprint arXiv:2406.12384

    2024 arXiv

  32. [40]

    D., Lucchi, A., 2018

    Li, Z., Wegner, J. D., Lucchi, A., 2018. Polymapper: Extracting city maps using polygons. arXiv preprint arXiv:1812.01497 , 2

    2018 arXiv

  33. [41]

    D., Lucchi, A., 2019

    Li, Z., Wegner, J. D., Lucchi, A., 2019. Topological map extraction from overhead images. Proceedings of the IEEE/CVF International Conference on Computer Vision, 1715--1724

    2019

  34. [42]

    Polytransform: Deep polygon transformer for instance segmentation

    Liang, J., Homayounfar, N., Ma, W.-C., Xiong, Y., Hu, R., Urtasun, R., 2020. Polytransform: Deep polygon transformer for instance segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9131--9140

    2020

  35. [43]

    L., 2014

    Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll \'a r, P., Zitnick, C. L., 2014. Microsoft coco: Common objects in context. European conference on computer vision, Springer, 740--755

    2014

  36. [44]

    Draw-and-understand: Leveraging visual prompts to enable mllms to comprehend what you want

    Lin, W., Wei, X., An, R., Gao, P., Zou, B., Luo, Y., Huang, S., Zhang, S., Li, H., 2024. Draw-and-understand: Leveraging visual prompts to enable mllms to comprehend what you want

    2024

  37. [45]

    Change-agent: Towards interactive comprehensive remote sensing change interpretation and analysis

    Liu, C., Chen, K., Zhang, H., Qi, Z., Zou, Z., Shi, Z., 2024. Change-agent: Towards interactive comprehensive remote sensing change interpretation and analysis. IEEE Transactions on Geoscience and Remote Sensing

    2024

  38. [46]

    J., 2023a

    Liu, H., Li, C., Li, Y., Lee, Y. J., 2023a. Improved baselines with visual instruction tuning

  39. [47]

    J., 2023b

    Liu, H., Li, C., Wu, Q., Lee, Y. J., 2023b. Visual instruction tuning

  40. [48]

    Path aggregation network for instance segmentation

    Liu, S., Qi, L., Qin, H., Shi, J., Jia, J., 2018. Path aggregation network for instance segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 8759--8768

    2018

  41. [49]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF international conference on computer vision, 10012--10022

    2021

  42. [50]

    Building Outline Delineation From VHR Remote Sensing Images Using the Convolutional Recurrent Neural Network Embedded With Line Segment Information

    Liu, Z., Tang, H., Huang, W., 2022. Building Outline Delineation From VHR Remote Sensing Images Using the Convolutional Recurrent Neural Network Embedded With Line Segment Information. IEEE Transactions on Geoscience and Remote Sensing , 60, 1--13

    2022

  43. [51]

    et al., 2024a

    Luo, J., Pang, Z., Zhang, Y., Wang, T., Wang, L., Dang, B., Lao, J., Wang, J., Chen, J., Tan, Y. et al., 2024a. Skysensegpt: A fine-grained instruction tuning dataset and model for remote sensing vision-language understanding. arXiv preprint arXiv:2406.10100

    arXiv

  44. [52]

    Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning based domain adaptation

    Luo, M., Ji, S., 2022. Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning based domain adaptation. ISPRS Journal of Photogrammetry and Remote Sensing , 191, 105--128

    2022

  45. [53]

    SAM-RSIS: Progressively adapting SAM with box prompting to remote sensing image instance segmentation

    Luo, M., Zhang, T., Wei, S., Ji, S., 2024b. SAM-RSIS: Progressively adapting SAM with box prompting to remote sensing image instance segmentation. IEEE Transactions on Geoscience and Remote Sensing

  46. [54]

    P., 2019 (accessed November 10, 2019)

    Mohanty, S. P., 2019 (accessed November 10, 2019). CrowdAI mapping challenge 2018 dataset. https://www.crowdai.org/challenges/mapping-challenge

    2019

  47. [55]

    Lhrs-bot: Empowering remote sensing with vgi-enhanced large multimodal language model

    Muhtar, D., Li, Z., Gu, F., Zhang, X., Xiao, P., 2024. Lhrs-bot: Empowering remote sensing with vgi-enhanced large multimodal language model. arXiv preprint arXiv:2402.02544

    2024 arXiv

  48. [56]

    et al., 2023

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A. et al., 2023. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193

    2023 arXiv

  49. [57]

    et al., 2024

    Pang, C., Wu, J., Li, J., Liu, Y., Sun, J., Li, W., Weng, X., Wang, S., Feng, L., Xia, G.-S. et al., 2024. H2RSVLM: Towards Helpful and Honest Remote Sensing Large Vision Language Model. arXiv preprint arXiv:2403.20213

    2024 arXiv

  50. [58]

    Deep snake for real-time instance segmentation

    Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., Zhou, X., 2020. Deep snake for real-time instance segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8533--8542

    2020

  51. [59]

    W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J

    Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J. et al., 2021. Learning transferable visual models from natural language supervision. International conference on machine learning, PMLR, 8748--8763

    2021

  52. [60]

    D., Ermon, S., Finn, C., 2024

    Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., Finn, C., 2024. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems , 36

    2024

  53. [61]

    Am-radio: Agglomerative vision foundation model reduce all domains into one

    Ranzinger, M., Heinrich, G., Kautz, J., Molchanov, P., 2024. Am-radio: Agglomerative vision foundation model reduce all domains into one. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12490--12500

    2024

  54. [62]

    M., Xing, E., Yang, M.-H., Khan, F

    Rasheed, H., Maaz, M., Shaji, S., Shaker, A., Khan, S., Cholakkal, H., Anwer, R. M., Xing, E., Yang, M.-H., Khan, F. S., 2024. Glamm: Pixel grounding large multimodal model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13009--13018

    2024

  55. [63]

    Pixellm: Pixel reasoning with large multimodal model

    Ren, Z., Huang, Z., Wei, Y., Zhao, Y., Fu, D., Feng, J., Jin, X., 2024. Pixellm: Pixel reasoning with large multimodal model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 26374--26383

    2024

  56. [64]

    Geollm-engine: A realistic environment for building geospatial copilots

    Singh, S., Fore, M., Stamoulis, D., 2024. Geollm-engine: A realistic environment for building geospatial copilots. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 585--594

    2024

  57. [65]

    Internlm: A multilingual language model with progressively enhanced capabilities

    Team, I., 2023. Internlm: A multilingual language model with progressively enhanced capabilities

    2023

  58. [66]

    Fcos: Fully convolutional one-stage object detection

    Tian, Z., Shen, C., Chen, H., He, T., 2019. Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE/CVF international conference on computer vision, 9627--9636

    2019

  59. [67]

    et al., 2023

    Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S. et al., 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288

    2023 arXiv

  60. [68]

    From image transfer to object transfer: Cross-domain instance segmentation based on center point feature alignment

    Wang, J., Ji, S., Zhang, T., 2023. From image transfer to object transfer: Cross-domain instance segmentation based on center point feature alignment. IEEE Transactions on Geoscience and Remote Sensing , 61, 1--11

    2023

  61. [69]

    Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

    Wang, P., Bai, S., Tan, S., Wang, S., Fan, Z., Bai, J., Chen, K., Liu, X., Wang, J., Ge, W., Fan, Y., Dang, K., Du, M., Ren, X., Men, R., Liu, D., Zhou, C., Zhou, J., Lin, J., 2024a. Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution. arXiv p...

    arXiv

  62. [70]

    et al., 2024b

    Wang, P., Hu, H., Tong, B., Zhang, Z., Yao, F., Feng, Y., Zhu, Z., Chang, H., Diao, W., Ye, Q. et al., 2024b. RingMoGPT: A Unified Remote Sensing Foundation Model for Vision, Language, and grounded tasks. IEEE Transactions on Geoscience and Remote Sensing

  63. [71]

    Solo: Segmenting objects by locations

    Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L., 2020. Solo: Segmenting objects by locations. European Conference on Computer Vision, Springer, 649--665

    2020

  64. [72]

    Skyscript: A large and semantically diverse vision-language dataset for remote sensing

    Wang, Z., Prabha, R., Huang, T., Wu, J., Rajagopal, R., 2024c. Skyscript: A large and semantically diverse vision-language dataset for remote sensing. Proceedings of the AAAI Conference on Artificial Intelligence, 38number 6, 5805--5813

  65. [73]

    Graph convolutional networks for the automated production of building vector maps from aerial images

    Wei, S., Ji, S., 2021. Graph convolutional networks for the automated production of building vector maps from aerial images. IEEE Transactions on Geoscience and Remote Sensing , 60, 1--11

    2021

  66. [74]

    Toward automatic building footprint delineation from aerial images using CNN and regularization

    Wei, S., Ji, S., Lu, M., 2019. Toward automatic building footprint delineation from aerial images using CNN and regularization. IEEE Transactions on Geoscience and Remote Sensing , 58(3), 2178--2189

    2019

  67. [75]

    A Concentric Loop Convolutional Neural Network for Manual Delineation-Level Building Boundary Segmentation From Remote-Sensing Images

    Wei, S., Zhang, T., Ji, S., 2021. A Concentric Loop Convolutional Neural Network for Manual Delineation-Level Building Boundary Segmentation From Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing , 60, 1--11

    2021

  68. [76]

    BuildMapper: A fully learnable framework for vectorized building contour extraction

    Wei, S., Zhang, T., Ji, S., Luo, M., Gong, J., 2023. BuildMapper: A fully learnable framework for vectorized building contour extraction. ISPRS Journal of Photogrammetry and Remote Sensing , 197, 87--104

    2023

  69. [77]

    From lines to Polygons: Polygonal building contour extraction from High-Resolution remote sensing imagery

    Wei, S., Zhang, T., Yu, D., Ji, S., Zhang, Y., Gong, J., 2024. From lines to Polygons: Polygonal building contour extraction from High-Resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing , 209, 213--232

    2024

  70. [78]

    A., 2022

    Wu, Y., Xu, L., Chen, Y., Wong, A., Clausi, D. A., 2022. TAL: Topography-Aware Multi-Resolution Fusion Learning for Enhanced Building Footprint Extraction. IEEE Geoscience and Remote Sensing Letters , 19, 1--5

    2022

  71. [79]

    Vectorizing historical maps with topological consistency: A hybrid approach using transformers and contour-based instance segmentation

    Xia, X., Zhang, T., Heitzler, M., Hurni, L., 2024a. Vectorizing historical maps with topological consistency: A hybrid approach using transformers and contour-based instance segmentation. International Journal of Applied Earth Observation and Geoinformation , 129, 103837

  72. [80]

    Video instance segmentation is all you need for linking geographic entities from historical maps

    Xia, X., Zhang, T., Hurni, L., 2024b. Video instance segmentation is all you need for linking geographic entities from historical maps. IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 8491--8494

    2024

  73. [81]

    Polarmask: Single shot instance segmentation with polar representation

    Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., Luo, P., 2020. Polarmask: Single shot instance segmentation with polar representation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 12193--12202

    2020

  74. [82]

    HiSup: Accurate polygonal mapping of buildings in satellite imagery with hierarchical supervision

    Xu, B., Xu, J., Xue, N., Xia, G.-S., 2023. HiSup: Accurate polygonal mapping of buildings in satellite imagery with hierarchical supervision. ISPRS Journal of Photogrammetry and Remote Sensing , 198, 284--296

    2023

  75. [83]

    et al., 2025

    Yang, A., Li, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Gao, C., Huang, C., Lv, C. et al., 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388

    2025 arXiv

  76. [84]

    et al., 2024

    Yang, A., Yang, B., Hui, B., Zheng, B., Yu, B., Zhou, C., Li, C., Li, C., Liu, D., Huang, F. et al., 2024. Qwen2 technical report. arXiv preprint arXiv:2407.10671

    2024 arXiv

  77. [85]

    A review of recurrent neural networks: LSTM cells and network architectures

    Yu, Y., Si, X., Hu, C., Zhang, J., 2019. A review of recurrent neural networks: LSTM cells and network architectures. Neural computation , 31(7), 1235--1270

    2019

  78. [86]

    Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

    Yuan, H., Li, X., Zhang, T., Huang, Z., Xu, S., Ji, S., Tong, Y., Qi, L., Feng, J., Yang, M.-H., 2025. Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos. arXiv preprint arXiv:2501.04001

    2025 arXiv

  79. [87]

    Learning building extraction in aerial scenes with convolutional networks

    Yuan, J., 2017. Learning building extraction in aerial scenes with convolutional networks. IEEE transactions on pattern analysis and machine intelligence , 40(11), 2793--2798

    2017

  80. [88]

    Osprey: Pixel understanding with visual instruction tuning

    Yuan, Y., Li, W., Liu, J., Tang, D., Luo, X., Qin, C., Zhang, L., Zhu, J., 2024. Osprey: Pixel understanding with visual instruction tuning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 28202--28211

    2024

  81. [89]

    Skyeyegpt: Unifying remote sensing vision-language tasks via instruction tuning with large language model

    Zhan, Y., Xiong, Z., Yuan, Y., 2024. Skyeyegpt: Unifying remote sensing vision-language tasks via instruction tuning with large language model. arXiv preprint arXiv:2401.09712

    2024 arXiv

  82. [90]

    HiT: Building Mapping with Hierarchical Transformers

    Zhang, M., Liu, Q., Wang, Y., 2024a. HiT: Building Mapping with Hierarchical Transformers. IEEE Transactions on Geoscience and Remote Sensing

  83. [91]

    Gpt4roi: Instruction tuning large language model on region-of-interest

    Zhang, S., Sun, P., Chen, S., Xiao, M., Shao, W., Zhang, W., Liu, Y., Chen, K., Luo, P., 2025a. Gpt4roi: Instruction tuning large language model on region-of-interest. European Conference on Computer Vision, Springer, 52--70

  84. [92]

    C., Yan, S., 2024b

    Zhang, T., Li, X., Fei, H., Yuan, H., Wu, S., Ji, S., Loy, C. C., Yan, S., 2024b. Omg-llava: Bridging image-level, object-level, pixel-level reasoning and understanding. arXiv preprint arXiv:2406.19389

    arXiv

  85. [93]

    Pixel-sail: Single transformer for pixel-grounded understanding

    Zhang, T., Li, X., Huang, Z., Li, Y., Lei, W., Deng, X., Chen, S., Ji, S., Feng, J., 2025b. Pixel-sail: Single transformer for pixel-grounded understanding. arXiv preprint arXiv:2504.10465

    arXiv

  86. [94]

    E2ec: An end-to-end contour-based method for high-quality high-speed instance segmentation

    Zhang, T., Wei, S., Ji, S., 2022. E2ec: An end-to-end contour-based method for high-quality high-speed instance segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4443--4452

    2022

  87. [95]

    P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images

    Zhang, T., Wei, S., Zhou, Y., Luo, M., Yu, W., Ji, S., 2024c. P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing

  88. [96]

    Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery

    Zhang, W., Cai, M., Zhang, T., Lei, G., Zhuang, Y., Mao, X., 2024d. Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery. arXiv preprint arXiv:2403.03790

    arXiv

  89. [97]

    Earthgpt: A universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain

    Zhang, W., Cai, M., Zhang, T., Zhuang, Y., Mao, X., 2024e. Earthgpt: A universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain. IEEE Transactions on Geoscience and Remote Sensing

  90. [98]

    RS5M and GeoRSCLIP: A large scale vision-language dataset and a large vision-language model for remote sensing

    Zhang, Z., Zhao, T., Guo, Y., Yin, J., 2024f. RS5M and GeoRSCLIP: A large scale vision-language dataset and a large vision-language model for remote sensing. IEEE Transactions on Geoscience and Remote Sensing

  91. [99]

    Building extraction from satellite images using mask r-cnn with building boundary regularization

    Zhao, K., Kang, J., Jung, J., Sohn, G., 2018. Building extraction from satellite images using mask r-cnn with building boundary regularization. Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 247--251

    2018

  92. [100]

    Building instance segmentation and boundary regularization from high-resolution remote sensing images

    Zhao, W., Persello, C., Stein, A., 2020. Building instance segmentation and boundary regularization from high-resolution remote sensing images. IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 3916--3919

    2020

  93. [101]

    Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework

    Zhao, W., Persello, C., Stein, A., 2021. Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework. ISPRS journal of photogrammetry and remote sensing , 175, 119--131

    2021

  94. [102]

    GeoGround: A Unified Large Vision-Language Model

    Zhou, Y., Lan, M., Li, X., Ke, Y., Jiang, X., Feng, L., Zhang, W., 2024. GeoGround: A Unified Large Vision-Language Model. for Remote Sensing Visual Grounding. arXiv preprint arXiv:2411.11904

    2024 arXiv

  95. [103]

    et al., 2025

    Zhu, J., Wang, W., Chen, Z., Liu, Z., Ye, S., Gu, L., Duan, Y., Tian, H., Su, W., Shao, J. et al., 2025. InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models. arXiv preprint arXiv:2504.10479

    2025 arXiv

  96. [104]

    MAP-Net: Multiple attending path neural network for building footprint extraction from remote sensed imagery

    Zhu, Q., Liao, C., Hu, H., Mei, X., Li, H., 2020. MAP-Net: Multiple attending path neural network for building footprint extraction from remote sensed imagery. IEEE Transactions on Geoscience and Remote Sensing , 59(7), 6169--6181

    2020

  97. [105]

    Polyworld: Polygonal building extraction with graph neural networks in satellite images

    Zorzi, S., Bazrafkan, S., Habenschuss, S., Fraundorfer, F., 2022. Polyworld: Polygonal building extraction with graph neural networks in satellite images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1848--1857

    2022

  98. [106]

    Machine-learned regularization and polygonization of building segmentation masks

    Zorzi, S., Bittner, K., Fraundorfer, F., 2021. Machine-learned regularization and polygonization of building segmentation masks. 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 3098--3105

    2021

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.