Pith. sign in

REVIEW 3 major objections 5 minor 42 references

Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read Multi-date NDVI stacks give deep segmentation models the seasonal context needed to extract functional field boundaries across large-field and smallholder landscapes.

desk verdict Solid but incremental Netherlands result; the cross-region claim is undermined by an internal contradiction and circular Pakistan labels. read the letter →

arxiv 2411.15923 v1 pith:Y2OJPPN4 submitted 2024-11-24 cs.CV cs.AIcs.LG

Saba Zahid , Sajid Ghuffar , Obaid-ur-Rehman , Syed Roshaan Ali Shah This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords fieldboundarydelineationfunctionalboundariesmulti-dateNDVIstackU-NetsemanticsegmentationtransferlearningsmallholderagricultureSentinel-2PlanetScope
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

Functional field boundaries—the active edges of crop fields—are hard to map automatically because fields change appearance through the growing season and because smallholder landscapes often lack training labels. This paper tests whether feeding a deep segmentation network three dates of NDVI (normalized difference vegetation index) stacked into one image improves boundary extraction compared with single-date or band-stack inputs. Using U-Net models with four backbones on Sentinel-2 and PlanetScope imagery, the authors report that the three-date NDVI stack gives the best mean Intersection-over-Union scores in the Netherlands, around 0.73–0.74, and that a model trained jointly on Dutch and Pakistani fields reaches 0.74 IoU while generalizing across both regions. The paper concludes that temporal NDVI context, multi-scale ground truth, and fine spatial resolution are key ingredients for field-boundary models that work in heterogeneous agricultural environments.

What carries the argument

The load-bearing object is the multi-date NDVI stack: for each of three months (April, August, October 2022 in the Netherlands; November 2022, February, March 2023 in Pakistan), NDVI is computed from the Red and Near-Infrared bands and the three single-date NDVI layers are concatenated into a three-channel input image. This compresses the twelve-channel three-date band stack into three channels while encoding phenological change—when crops are sown, growing, and harvested—so that fields that look similar on any single date become separable. The segmentation models are U-Nets with ResNet-family encoders (EfficientNet-B2, ResNet-50, ResNet-152, SE-ResNeXt-50), trained with categorical focal dice loss for 50 epochs; the boundary mask has three classes: non-crop, inner field, and boundary, and post-processing vectorizes and simplifies the raw predictions.

What would settle it

Re-draw the Pakistan test labels from independent high-resolution true-color imagery or field visits, with annotators never seeing the NDVI composites, then re-run the combined Netherlands–Pakistan model; if mean IoU on Pakistan drops well below the reported 0.74, the cross-region generalization is an artifact of shared label-image appearance.

Watch

Extended reading notes

Core claim

The study's central claim is that stacking NDVI computed from three well-chosen dates across the growing season gives a deep-learning model the temporal context it needs to separate actively farmed fields as functional units, and that this input is at least as good as—and more compact than—stacking the full spectral bands. In the Netherlands experiments, the strongest configuration (SE-ResNeXt-50 with a three-date NDVI stack) reached mean validation IoU near 0.73 with 3 m PlanetScope data and near 0.74 with 10 m Sentinel-2 data, outperforming single-date images and three-date band composites. The paper also claims that transfer learning from a large-field landscape (Netherlands) to a smallholder landscape (Dunyapur, Pakistan) fails when applied directly, but that training on combined Netherlands and Pakistan data produces a model with mean IoU of 0.74 that generalizes across both regions. The authors attribute this to the value of multi-scale ground information and to NDVI stacks encoding crop growth over time.

Load-bearing premise

The Pakistan ground-truth boundaries were manually digitized from the same three-date NDVI composite images that the model receives as input, so if that digitization simply follows the colors in those images, the Pakistan and combined-model accuracy scores may overstate how well the model finds true field boundaries.

Editorial extensions

If this is right

  • Field-boundary models should treat multi-date NDVI stacks as a strong input baseline: the paper reports that this three-channel stack outperforms single-date band images and matches or beats the twelve-channel three-date band stack in the Netherlands experiments.
  • Direct transfer of a model trained on large-field Netherlands parcels to smallholder Pakistan fields performs poorly, so practical deployments in smallholder regions need local or combined training data rather than a purely transferred model.
  • Combining ground truth from the Netherlands and Pakistan yields a single model with mean IoU of 0.74 that the paper reports as generalizing across both regions, suggesting multi-region training is a viable route to universal field-boundary models.
  • Fine spatial resolution (PlanetScope at 3 m) becomes important in small-field landscapes; in the Netherlands, 10 m Sentinel-2 already suffices, so resolution requirements depend on field-size distribution.
  • Raw segmentation predictions should be post-processed (simplify, trim, eliminate fragments, vectorize) to produce closed, usable field-boundary polygons.

Reading between the lines

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

  • Inference: A controlled comparison that keeps the number of input channels identical—say, three single-date green or shortwave-infrared bands versus three NDVI dates—would isolate whether the gain comes from phenological information or simply from reducing the input dimensionality; the paper's design compares the NDVI stack against band stacks and single dates, not against a same-channel-count non
  • Inference: The Pakistan label-generation procedure raises a validation risk: if annotators traced boundaries visible in the NDVI composite, the combined model may be learning annotation style rather than field physics; independent field-collected or very-high-resolution labels would resolve this before operational use.
  • Inference: If the temporal-stack benefit holds, the same three-date NDVI input could extend to other field-level tasks—crop-type classification, irrigation monitoring, or yield estimation—where the field unit must be identified before per-field analysis.
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

3 major / 5 minor

Summary. The manuscript proposes using multi-date NDVI stacks as input to U-Net-style deep learning models for functional field boundary delineation, tested in the Netherlands (using BRP agricultural parcel labels) and Pakistan (using self-digitized boundaries). The authors evaluate four backbones across Sentinel-2 and PlanetScope imagery and report that a three-date NDVI stack reaches mean IoU around 0.73–0.74 in the Netherlands, outperforming single-date inputs. They then apply transfer learning to Dunyapur, Pakistan, train separate and combined Netherlands–Pakistan models, and claim the combined model generalizes best.

Significance. If the Netherlands comparison is taken alone, the paper provides a useful, simple result: feeding a three-date NDVI stack into a segmentation network improves field-boundary delineation over single-date inputs, and this is grounded in independent BRP labels. The broader claims about multi-scale cross-region generalization and the value of combined training for smallholder systems, however, rest on Pakistan results whose ground truth was digitized from the same NDVI composites used as model input and on internally contradictory statements; those claims are currently unsupported. The paper also ships no code or error bars, so the quantitative effect sizes are not independently verifiable.

major comments (3)
  1. [Section IV, Figure 12] The cross-region generalization claim is based on mutually contradictory text. The paragraph states 'better mean IoU was achieved on Dunyapur data alone with self-crafted training data than Netherlands data or combined data for both areas' and then, a few sentences later, 'when model was trained by combining two geographical areas together achieved better mean IoU score (0.74) and also generalized well.' If both sentences refer to the same evaluation metric and test split, at least one is false; if they refer to different subsets or different metrics, the manuscript does not state this. The paper must specify which IoU is reported (per-region test IoU, combined test IoU, validation IoU), provide the numerical IoU for each model on each region, and revise the conclusion accordingly.
  2. [Section II.C.3, Figure 6] The Pakistan ground-truth labels were manually digitized from the same multi-date NDVI composite images used as model input, and Section IV concedes that these self-crafted labels 'visibly correlated with the boundaries in the ndvi stack images.' This creates circularity: the reported IoU for the Dunyapur-only and combined models partly measures agreement with the annotator's visual reading of the input imagery, not field-boundary accuracy against an independent source. The paper needs an independent validation set for Pakistan (e.g., boundaries digitized from very high-resolution imagery, field surveys, or an existing cadastral/administrative layer) or must restrict its quantitative claims to the Netherlands branch and present the Pakistan results as qualitative only.
  3. [Section III.A, Table 3, Figure 10] All mean IoU values, including the central comparison between the NDVI stack and the three-date band stack, come from a single training run with 50 epochs per model and no repeated seeds, error bars, or statistical significance tests. Given that the reported differences are small (e.g., approximately 0.73 vs. values that appear within a few hundredths), the claim that the NDVI stack is better than multi-date band stacking is not statistically supported. The authors should report mean and standard deviation over at least 3–5 seeds, or otherwise temper the claim to a qualitative observation.
minor comments (5)
  1. [Table numbering] Table numbering is inconsistent: Section III.A refers to 'Table 1' for the imagery combinations, but the table with satellite/date/tile combinations is labeled 'TABLE 2', while Table 1 is the farm-size statistics. This will confuse readers.
  2. [Section III.A] The model list is inconsistent: Section III.A says 'EfficientnetB7' and Table 2/Figure 10 list 'EfficientNet B2.' Please correct the name and ensure it matches everywhere.
  3. [Section II.D] In the description of PlanetScope tiling, the text says 'This produced 4244 tiles for sentinel covering 1.3271 km2 of land area per tile'; this should say 'PlanetScope' rather than 'sentinel.'
  4. [Section III.C] The evaluation metric subsection states that IoU is given by Eq-1, but the equation is not actually displayed in the text. Please insert the formula.
  5. [References] Several references are malformed or duplicated, for example [16] and [20] point to the same paper with different formatting, and [26] cites 'Philipp FISCHER a Thomas BROX' as a U-Net paper. The reference list needs a full cleanup.

Circularity Check

1 steps flagged · score 4.0 of 10

The Netherlands NDVI-stack comparison is externally grounded, but the Pakistan and combined-model generalization claim is partially circular because the Pakistan labels were manually digitized from the same multi-date NDVI composite used as model input.

  1. self definitional [Section II.C.3 (Field boundaries ground truth data), Figure 6; confirmed in Section IV (Results and Discussions)]
    "Due to lack of field boundaries data in Pakistan, labeled vector data for field boundaries was indigenously digitized using visual interpretation on multi-date NDVI composite image and manual annotation (Fig 6). ... The manually crafted training dataset for Dunyapur visibly correlated with the boundaries in the ndvi stack images."

    The Dunyapur ground-truth mask is created by manually tracing boundaries visible in the same multi-date NDVI composite that is later used as the model input. Any model trained and evaluated on Dunyapur is therefore scored against the annotator's reading of the NDVI stack, not against independent field-boundary truth. The reported IoU for the Dunyapur-only and combined models (including the 0.74 'generalized well' claim) is partially self-definitional: high agreement can reflect the labeler following the same NDVI boundaries that the model also sees. The paper's own statement that the crafted labels 'visibly correlated with the boundaries in the ndvi stack images' confirms the target is not independent of the input representation.

full rationale

The central input-strategy claim of the paper, that multi-date NDVI stacks improve field-boundary delineation, is supported by the Netherlands experiments, where the BRP vector layer provides an externally sourced, independent ground truth. That comparison is not circular. The circularity burden is confined to the cross-region generalization claim involving Pakistan. Section II.C.3 states that the Pakistan field-boundary labels were 'indigenously digitized using visual interpretation on multi-date NDVI composite image,' and Section IV concedes that these manually crafted labels 'visibly correlated with the boundaries in the ndvi stack images.' Since the model input for the Dunyapur and combined-model experiments is exactly this NDVI stack, the reported IoU on Pakistan measures, at least in part, agreement between the model and the annotator's prior interpretation of the same input representation, rather than independent field-boundary accuracy. This makes the combined-model and universal-model conclusions partially self-referential. The paper also contains an internal contradiction in Section IV, first stating that Dunyapur-alone training achieved better mean IoU than combined training, then stating that combining the two areas achieved a better mean IoU (0.74) and generalized well; this is a correctness and interpretability problem rather than a circularity, but it further weakens the generalization claim. The self-citations in the reference list are not load-bearing: the temporal-stacking result is independently tested on Netherlands data, and the post-processing citation is peripheral. Overall, the central Netherlands finding stands on independent ground truth, but the Pakistan-based generalization claim is partially circular, giving a moderate circularity score of 4.

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

The paper contributes no new theory or entities; it relies on standard deep learning assumptions and on two labeling sources, one of which (Pakistan) is generated from the input imagery. The main free parameters are training choices and label-construction parameters.

free parameters (4)
  • Training epochs = 50
    Chosen by hand under a 12-hour Colab runtime limit; IoU comparisons may depend on convergence, which is not analyzed.
  • Batch size = 10
    Chosen for limited GPU memory; no sensitivity analysis is reported.
  • Boundary buffer width in label masks = One native pixel (10 m for Sentinel-2, 3 m for PlanetScope)
    The buffer operation used to convert field polygons into boundary masks directly controls boundary thickness and therefore affects IoU values.
  • Tile size and overlap = 256x256 with 128 overlap (Sentinel-2), 384x384 with 128 overlap (PlanetScope)
    Tile size balances context and compute; the chosen values affect how much surrounding field structure the model sees.
assumptions (4)
  • domain assumption BRP parcel polygons reliably represent functional field boundaries.
    The Netherlands training labels are taken from the Basic Registration Crop Parcels layer (Section II.C.3), which is a registration layer and may include parcels that do not correspond to active functional field units.
  • domain assumption Manual digitization of field boundaries on multi-date NDVI composites is accurate ground truth.
    Pakistan labels are self-crafted via visual interpretation (Section II.C.3, Figure 6); no independent field survey or accuracy assessment is provided.
  • domain assumption Three selected dates capture the relevant temporal crop dynamics.
    Dates were chosen from crop calendars (Section II.B), but no sensitivity analysis shows that three dates or these specific months are sufficient to represent the growing season.
  • ad hoc to paper Fifty epochs are sufficient for model convergence.
    Models were run for 50 epochs under a limited runtime environment (Section III.A); no training curves or convergence checks are shown.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan." pith.science (2026). https://pith.science/paper/Y2OJPPN4

@misc{pith2026241115923,
  author       = {Pith},
  title        = {Pith review of: Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y2OJPPN4}},
  note         = {Machine review of arXiv:2411.15923}
}
read the original abstract

This study explores the effectiveness of multi-temporal satellite imagery for better functional field boundary delineation using deep learning semantic segmentation architecture on two distinct geographical and multi-scale farming systems of Netherlands and Pakistan. Multidate images of April, August and October 2022 were acquired for PlanetScope and Sentinel-2 in sub regions of Netherlands and November 2022, February and March 2023 for selected area of Dunyapur in Pakistan. For Netherlands, Basic registration crop parcels (BRP) vector layer was used as labeled training data. while self-crafted field boundary vector data were utilized for Pakistan. Four deep learning models with UNET architecture were evaluated using different combinations of multi-date images and NDVI stacks in the Netherlands subregions. A comparative analysis of IoU scores assessed the effectiveness of the proposed multi-date NDVI stack approach. These findings were then applied for transfer learning, using pre-trained models from the Netherlands on the selected area in Pakistan. Additionally, separate models were trained using self-crafted field boundary data for Pakistan, and combined models were developed using data from both the Netherlands and Pakistan. Results indicate that multi-date NDVI stacks provide additional temporal context, reflecting crop growth over different times of the season. The study underscores the critical role of multi-scale ground information from diverse geographical areas in developing robust and universally applicable models for field boundary delineation. The results also highlight the importance of fine spatial resolution for extraction of field boundaries in regions with small scale framing. The findings can be extended to multi-scale implementations for improved automatic field boundary delineation in heterogeneous agricultural environments.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages

  1. [10]

    Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data.,

    P. M. V. Timofeev, D. Bocharov, I. Kunina, A. Smagina and M. Zagarev, "Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data.," 2021

    work page 2021

  2. [23]

    Deep Learning on High Spatial and Temporal Cadence Satellite Imagery for Field Boundary Delineation,

    S. S. R. A., R. A. . F. Ishaq, Y. Shabbir and I. Ahmad, "Deep Learning on High Spatial and Temporal Cadence Satellite Imagery for Field Boundary Delineation," in 2021 Seventh International Conference on Aerospace Science and Engineering (ICASE), 2021

    work page 2021

  3. [1]

    Unlocking large-scale crop field delineation in small holder farming systems with transfer learning and weak supervision.,

    W. S. W. F. L. and D. B., "Unlocking large-scale crop field delineation in small holder farming systems with transfer learning and weak supervision.," Remote Sensing, p. 5738, 2022

    work page 2022

  4. [2]

    Adeel, M

    A. and M. , "Adeel, M. "Evaluating the role of cadastre maps in Pakistan’s Land Administration: A GIS perspective," pp. 11-16, 2010

    work page 2010

  5. [3]

    Agricultural Field Boundary Delineation Using Deep Learning Techniques.,

    S. A. V. Kumbhar, S. Jadhav and . S. K. Shah, "Agricultural Field Boundary Delineation Using Deep Learning Techniques.," pp. 1-6, 2023

    work page 2023

  6. [4]

    Delineation of crop field areas and boundaries from UAS imagery using PBIA and GEOBIA with random forest classification.,

    V. O. L. B., J. Wang, A. Haddadi, A. LaRocque and G. Patterson, "Delineation of crop field areas and boundaries from UAS imagery using PBIA and GEOBIA with random forest classification.," Remote Sensing , p. 2640, 2020

    work page 2020

  7. [5]

    Cai, Yaping, et al

    C. Y. G. K. Guan, J. Peng, S. Wang, S. C., W. B. and L. Z., "Cai, Yaping, et al. "A high -performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach," Remote sensing of environment , pp. 35-47., 2018

    work page 2018

  8. [6]

    Farmer a ttitudes to the use of sensors and automation in fertilizer decision -making: Nitrogen fertilization in the Australian grains secto,

    B. R. G. V. and O. J., "Farmer a ttitudes to the use of sensors and automation in fertilizer decision -making: Nitrogen fertilization in the Australian grains secto," Precision agriculture, pp. 157-175., 2019

    work page 2019

Show all 42 references
  1. [7]

    Should increasing the field size of monocultural crops be expected to exacerbate pest damage?.,

    S. M. and J. A., "Should increasing the field size of monocultural crops be expected to exacerbate pest damage?.," Agriculture, ecosystems & environment, pp. 38-44, 2012

    2012

  2. [8]

    Bringing diversity back to agriculture: Smaller fields and non-crop elements enhance biodiversity in intensively managed arable farmlands,

    Š. M. V. Hula, M. Kipson, R. Daňková, J. Niedobová and A. Gamero, "Bringing diversity back to agriculture: Smaller fields and non-crop elements enhance biodiversity in intensively managed arable farmlands," Ecological Indicators, pp. 65 -73, 2018

    2018

  3. [9]

    A machine learning approach for agricultural parcel delineation through agglomerative segmentation,

    G.-P. A. G.-M. C. and M. Lillo-Saavedra., "A machine learning approach for agricultural parcel delineation through agglomerative segmentation," Internationa l journal of remote sensing, pp. 1809-1819, 2017

    2017

  4. [11]

    Automated crop field extraction from multi -temporal Web Enabled Landsat Data.,

    Y. L. and R. D. P., "Automated crop field extraction from multi -temporal Web Enabled Landsat Data.," Remote Sensing of Environment, pp. 42-64, 2014

    2014

  5. [12]

    Automated farm field delineation and crop row detection from satellite images,

    Alemu and M. Meseret, "Automated farm field delineation and crop row detection from satellite images," 2016

    2016

  6. [13]

    Supervised Learning of Edges and Object Boundaries,

    D. Piotr, T. Z. and B. Serge, "Supervised Learning of Edges and Object Boundaries," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2006

    2006

  7. [14]

    Fast edge detection using structured forests.,

    D. P. and Z. C. L., "Fast edge detection using structured forests.," in IEEE transactions on pattern analysis and machine intelligence, 2014

    2014

  8. [15]

    Yang, Ruoyu, et al

    Y. R., A. Z. U., S. U. C., M. Kamal and R. Rai, "Yang, Ruoyu, et al. "Detecting functional field units from satellite images in smallholder farming systems usi ng a deep learning based computer vision approach: A case study from Bangladesh.," Remote Sensing Applications: Socie...

    2020

  9. [16]

    P. . C. T. . V. A., B. J. R., D. B. and R. A., "Persello, C., Tolpekin, V. A., Bergado, J. R., & De By, R. (2019). Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping. Remote Sensing of Env...

    2019

  10. [17]

    Agricultural Field Boundary Delineation with Satellite Image Segmentation for High -Resolution Crop Mapping: A Case Study of Rice Paddy.,

    W. M. W. J., C. Yunpeng , J. Liu and L. Chen, "Agricultural Field Boundary Delineation with Satellite Image Segmentation for High -Resolution Crop Mapping: A Case Study of Rice Paddy.," p. 2342, 2022

    2022

  11. [18]

    Convolutional oriented boundaries: From image segmentation to high-level tasks,

    M. K. K., J. Pont -Tuset, P. Arbeláez an d V. G. L., "Convolutional oriented boundaries: From image segmentation to high-level tasks," in IEEE transactions on pattern analysis and machine intelligence , 2017

    2017

  12. [19]

    A deep learning approach to the classification of sub-decimetre resolution aerial images.,

    B. J. R., P. C. Persello and C. Gevaert, "A deep learning approach to the classification of sub-decimetre resolution aerial images.," pp. 1516-1519, 2016

    2016

  13. [20]

    Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping.,

    P. C. V. A., T. J. R. Bergado and D. B. R. A., "Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping.," Remote sensing of environment, p. 111253, 2019

    2019

  14. [21]

    Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super -Resolution Contour Detector Based on Fully Convolutional Networks,

    M. K. M., P. C. and T. V. A., "Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super -Resolution Contour Detector Based on Fully Convolutional Networks," Remote Sensing, 2020

    2020

  15. [22]

    A comparison of object -based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery,

    W. B. and V. N. A., "A comparison of object -based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery," Computers and Electronics in Agriculture, pp. 294-302, 2019

    2019

  16. [24]

    Deep learning for automatic outlining agricultural parcels: Exploiting the land parcel identification system.,

    G.-P. A. . L.-S. M., D. Rodriguez-Esparragon and G.- M. C., "Deep learning for automatic outlining agricultural parcels: Exploiting the land parcel identification system.," pp. 158223-158236., 2019

    2019

  17. [25]

    Aung, Han Lin, et al

    A. H. L., B. Uzkent, M. Burke, D. Lobell and S. Ermon, "Aung, Han Lin, et al. "Farm parcel delineation using spatio - temporal convolutional networks," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020

    2020

  18. [26]

    Philipp FISCHER a Thomas BROX,

    R. and O. , "Philipp FISCHER a Thomas BROX," U - Net: C onvolutional Networks for Biomedical Image Segmentation, 2015

    2015

  19. [27]

    Comparison of Backbones for Semantic Segmentation Network,

    Z. R., D. L., X. Q. and L. J., "Comparison of Backbones for Semantic Segmentation Network," Physics, 2020

    2020

  20. [28]

    Road extraction from high -resolution remote sensing imagery using deep learning,

    X. Y. Z. Xie, Y. Feng and Z. Chen, "Road extraction from high -resolution remote sensing imagery using deep learning," Remote Sensing, p. 1461, 2018

    2018

  21. [29]

    Geo services -PDOK,

    "Geo services -PDOK," September 2019. [Online]. Available:https://www.pdok.nl/ogc-webservices/- /article/basisregistratie-gewaspercelen-brp-

    2019

  22. [30]

    Wider or deeper: Revisiting the resnet model for visual recognition.,

    Z. Wu, C. Shen and A. V. D. Hengel, "Wider or deeper: Revisiting the resnet model for visual recognition.," Pattern Recognition, pp. 119-133, 2019

    2019

  23. [31]

    Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet -152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification,

    A. S. R. S. Antonyswamy, V. Sarveshwaran, M. Almeshari, Y. Alzamil and V. Ravi, "Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet -152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification," Diagnostics, p. 668, 2023

    2023

  24. [32]

    An efficient brain tumor image segmentation based on deep residual networks (ResNets),

    S. L. H., O. M. Fahmy, S. M. Gasser and M. S. El- Mahallawy, "An efficient brain tumor image segmentation based on deep residual networks (ResNets)," Journal of King Saud University-Engineering Sciences, pp. 404-412, 2021

    2021

  25. [33]

    ontextual band addition and m ulti-look inferencing to improve semantic segmentation model performance on satellite images,

    S. R. A. R. O. U. S. Y. &. I. R. A. Shah, "ontextual band addition and m ulti-look inferencing to improve semantic segmentation model performance on satellite images," Journal of Spatial Science, p. 1–24, 2024

    2024

  26. [34]

    Application of deep learning for delineation of visible cadastral bound aries from remote sensing imagery,

    C. S. K. M. Y. M. Y. and G. Vosselman, "Application of deep learning for delineation of visible cadastral bound aries from remote sensing imagery," Remote sensing, p. 2505, 2019

    2019

  27. [35]

    Inconsistencies in Cadastral Boundary Data—Digitisation and Maintenance.,

    F. B. J. Tekavec, M. K. Fras and A. Lisec, "Inconsistencies in Cadastral Boundary Data—Digitisation and Maintenance.," p. 2318, 2022

    2022

  28. [36]

    Improving field boundary delineation in ResUNets via adversarial deep learning,

    J. M. K. Guan, S. Wang, Y. Huang and B. P eng, "Improving field boundary delineation in ResUNets via adversarial deep learning," International Journal of Applied Earth Observation and Geoinformation, p. 102877, 2022

    2022

  29. [37]

    Y. R. A. Z. U., S. U. C., S. M. Kamal and R. Rai, "Yang, R., Ahmed, Z. U., Schulthess, U., Kamal, M., & Rai, R. (2020). Detecting functional field units from satellite images in smallholder farming systems using a deep learning based computer vision approach: A case study from...

    2020

  30. [38]

    U -SSD: Improved SSD based on U -Net architecture for end -to-end table detection in document images.,

    L. S. H. and C. H. C., "U -SSD: Improved SSD based on U -Net architecture for end -to-end table detection in document images.," Applied Sciences, p. 11446, 2021

    2021

  31. [39]

    Detecting functional field units from satellite images in smallholder farming systems using a deep learning based computer vision approach: A case study from Bangladesh,

    Y. R. Z. U. Ahmed, U. C. Schulthe ss, M. Kamal and R. Rai, "Detecting functional field units from satellite images in smallholder farming systems using a deep learning based computer vision approach: A case study from Bangladesh," Remote Sensing Applications: Society and Envir...

    2020

  32. [40]

    A case study for updating land parcel identification systems (IACS) by means of remote sensing.,

    O. M. and M. Hahn, "A case study for updating land parcel identification systems (IACS) by means of remote sensing.," pp. 6-120, 2004

    2004

  33. [41]

    Land Parcel Identification System (LPIS) Anomalies' Sampling and Spati al Pattern: Towards convergence of ecological methodologies and GIS technologies,

    G. D. and R. Zielinski, "Land Parcel Identification System (LPIS) Anomalies' Sampling and Spati al Pattern: Towards convergence of ecological methodologies and GIS technologies," 2008

    2008

  34. [42]

    U -SSD: Improved SSD based on U -Net architecture for end -to-end table detection in document images,

    L. S. -H. and H. -C. Chen, "U -SSD: Improved SSD based on U -Net architecture for end -to-end table detection in document images," Applied Sciences, p. 11446, 2021

    2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.