Auto-Prompting SAM for Weakly Supervised Landslide Extraction

Jian Wang; Pedram Ghamisi; Weikang Yu; Xianping Ma; Xiaokang Zhang

REVIEW 3 major objections 6 minor 1 cited by

Auto-Prompting SAM for Weakly Supervised Landslide Extraction

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Auto-prompting SAM lifts landslide F1 by at least 3 points

desk verdict Clean, plausible SAM-prompting recipe for landslide extraction, but the headline performance claim is overstated and T is selected on test data. read the letter →

arxiv 2501.13426 v2 pith:YSS5O6JK submitted 2025-01-23 cs.CV

Jian Wang , Xiaokang Zhang , Xianping Ma , Weikang Yu , Pedram Ghamisi This is my paper · ORCID

classification cs.CV

keywords weaklysupervisedlandslideextractionSegmentAnythingModelpromptengineeringclassactivationmapsobjectlocalizationpseudo-labelingremotesensing

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 aims to show that pixel-level landslide maps can be produced from image-level labels alone by feeding automatically generated prompts into a frozen Segment Anything Model (SAM). The method, APSAM, takes coarse class activation maps from an object localization network, binarizes them, and converts the resulting contours into box prompts and centroid point prompts. These prompts guide SAM to produce pseudo-masks, which then train a standard segmentation network. The authors report gains of at least 3.0% in F1 score and 3.69% in IoU over state-of-the-art weakly supervised methods on two high-resolution landslide datasets. If correct, this offers a low-annotation route to accurate landslide mapping.

What carries the argument

The adaptive prompt generation (APG) algorithm is the central mechanism. It binarizes the CAM at a fixed threshold, extracts contours, computes each contour's bounding box as a box prompt and its moment-based centroid as a point prompt, and feeds the combined prompts into SAM's prompt encoder. This converts a coarse activation heatmap into the sparse, structured input SAM needs to produce clean segmentation masks without any parameter update.

What would settle it

A direct test would be to compute pseudo-masks with the same CAM and threshold but replace SAM's prompts with random boxes and centroids of the same size and count: if the performance gap over baselines persists, the prompts are not doing the work attributed to them.

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Extended reading notes

Core claim

The central claim is that a frozen, prompt-only SAM can replace the usual pseudo-labeling pipeline in weakly supervised landslide extraction. Rather than refining CAMs or fine-tuning SAM, APSAM uses the CAM heatmap to guess where landslides are (boxes) and where their centers are (points), and relies on SAM's zero-shot segmentation to turn those guesses into fine-grained masks. The paper demonstrates this on the Hong Kong and Turkey datasets, where the approach surpasses six weakly supervised baselines in F1 and IoU, including the CAM-based LGAGNet and the SAM-based S2C.

Load-bearing premise

The method assumes that binarizing the class activation map at threshold 120 produces contours whose bounding boxes and centroids point to actual landslide extents and centers; if the heatmap is incomplete or the threshold is miscalibrated, SAM is guided to the wrong regions.

Editorial extensions

If this is right

APSAM yields pseudo-masks that train a ResUNet to outperform six weakly supervised baselines on both datasets.
The approach works on both aerial and satellite imagery (Hong Kong and Turkey) with 0.5–0.59 m resolution.
Combining box and point prompts outperforms either prompt type alone in the reported ablations.
The method requires no fine-tuning of SAM, so it inherits SAM's zero-shot segmentation behavior while adding only an off-the-shelf CAM module.
IoU gains range from 3.69 to 16.41 percentage points over the baselines on the Turkey dataset.

Reading between the lines

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

Because the threshold T is tuned on the evaluation data, the method's edge over baselines may shrink if the CAM distribution shifts; a non-fixed threshold or a learned prompt generator would be a natural stress test.
The same auto-prompting recipe may transfer to other weakly supervised segmentation tasks with irregular, fragmented objects, such as building damage or flooded areas, provided SAM produces reasonable masks for those categories.
The fixed threshold and contour geometry imply that landslides that merge into one CAM blob will be treated as a single object; adding a splitting rule based on size or shape could refine the prompts.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The one thing you should know: this is a clean, competent engineering paper that combines two existing ideas—CAM-based localization and SAM prompting—into a pipeline that produces pseudo-labels for landslide segmentation with only image-level supervision. The actual new content is the specific recipe and the APG algorithm that converts CAM contours into box and point prompts for frozen SAM. The ablation in Table II supports the design: point+box beats either alone by a clear margin on both datasets. The qualitative figures look reasonable. That is the good part.

The weak part is the quantitative claim. The abstract says “at least 3.0% F1 and 3.69% IoU” improvement, but on the Hong Kong dataset the gain over the strongest baseline (LGAGNet) is 0.52 F1 and 0.70 IoU. Only Turkey reaches the stated margins, and all results are single runs without error bars or significance tests. The bigger problem is the threshold T=120. It is selected from the IoU-versus-T curve in Fig. 5, and since pixel-level labels exist only for the test sets (no validation split is described), that curve is computed on the test data. So the measured IoU is partly a fitted quantity. The paper notes a plateau between T=50 and 170, which limits how much this matters, but the protocol is still not a clean independent evaluation.

The other practical gripe: the abstract promises source code and datasets at a GitHub link, but no link or code is actually provided in the manuscript, and the SAM checkpoint configuration is unspecified. That makes the result hard to reproduce or stress-test.

I don't think the method is broken. The logic is coherent, the ablation is sensible, and the problem is genuinely important. But the headline numbers should be treated cautiously. I'd want the authors to release code, add error bars, use a validation split for T (or at least explain why the plateau makes test-set selection harmless), and rewrite the abstract to match the actual margins. These are referee-level requests, not fatal flaws.

My take: this paper deserves a serious referee, especially in an applied remote sensing venue. It is a useful, incremental contribution. I would cite it for the approach if I worked on landslide mapping, but I would not repeat the stated performance gains as established. For a reading group, it's a decent example of how to adapt SAM under weak supervision, but not a must-read.

Recommendation: send to peer review with a request that the reviewers focus on the evaluation protocol and reproducibility.

Referee Report

3 major / 6 minor

Summary. The paper proposes APSAM, a weakly supervised landslide extraction pipeline. An object localization network trained with image-level labels produces CAMs; an adaptive prompt generation (APG) algorithm binarizes the CAM at a fixed threshold T, finds contours, and derives bounding-box and centroid prompts, which are fed into a frozen SAM to synthesize pseudo-masks. A ResUNet is then trained on these pseudo-masks. Experiments on the Hong Kong and Turkey datasets compare APSAM against six weakly supervised baselines using OA, precision, recall, F1, and IoU. The authors claim improvements of at least 3.0% F1 and 3.69% IoU over state-of-the-art methods and report ablations showing that hybrid point-plus-box prompts outperform either prompt type alone.

Significance. The idea is practically appealing: it avoids CAM-based pseudo-label refinement or SAM fine-tuning by using a frozen SAM with automatically generated prompts, making it a low-cost way to turn image-level labels into pixel-level landslide maps. If the claimed gains hold, the method would be a useful contribution to weakly supervised landslide extraction. The paper's strengths are its simple and reproducible pipeline, the planned release of code and data, and the ablation study in Table II, which supports the hybrid prompt design. The main concerns are evaluation-protocol issues rather than conceptual flaws: the headline quantitative claim is not fully supported by the reported tables, the threshold T is not validated independently of the test labels as described, and the results are single runs without uncertainty measures.

major comments (3)

[Abstract; Table I] The abstract's claim of 'at least 3.0% in F1 score and 3.69% in IoU compared to other state-of-the-art methods' is not supported by Table I. On the Hong Kong dataset, the gain over the strongest baseline LGAGNet is 0.52 F1 points (79.14 vs. 78.62) and 0.70 IoU points (65.48 vs. 64.78); only the Turkey dataset reaches the stated margins (3.01 F1 and 3.69 IoU). The wording 'at least' is therefore inaccurate and should be revised, or the claim should be restricted to the dataset where it actually holds.
[Sec. III-A, III-B, III-D (Fig. 5)] The key hyperparameter T is not validated independently of the test labels. Section III-A describes image-level training sets and pixel-level test sets but no validation split. Section III-B fixes T=120, and Section III-D justifies this choice using the IoU-versus-threshold curve in Fig. 5. Since pixel-level labels are described as existing only for the test sets, the IoU values in Fig. 5 appear to be computed on the evaluation data, making the 'Ours' row in Table I conditional on a threshold selected to maximize test IoU. The reported plateau from T=50 to 170 may limit the practical impact, but the protocol as described is not an independent evaluation; please either fix T a priori, choose it on a held-out split, or explicitly report the sensitivity of Table I to T.
[Table I; Sec. III-C] All quantitative results are reported as single runs without error bars, standard deviations, or significance tests. The Hong Kong F1 margin of 0.52 points over LGAGNet is small enough that it could arise from training stochasticity, and the conclusion that 'our network achieves the highest F1-Score and IoU values' on Hong Kong is not statistically established. Repeating the experiments over multiple seeds and reporting mean and standard deviation, or another measure of uncertainty, is needed to support the claimed superiority.

minor comments (6)

[Sec. I] The introduction contains a typo: 'zeo-shot' should be 'zero-shot.'
[Sec. III-A] The dataset name is rendered inconsistently as both 'TurKey' and 'Turkey'; please unify the spelling.
[Sec. III-C and III-B] There are minor textual inconsistencies: 'Tabel I' should be 'Table I,' and 'FilpCAM' in the experimental setup should be 'FlipCAM' to match the table and references.
[Sec. III-D; Table II] The prose describing Table II mislabels the metrics: for the Turkey 'Point + Box' row, the table lists Precision 64.00, Recall 71.03, F1 67.33, and IoU 50.75, but the text says 'the precision 71.03%, recall 67.33%, and F1-score 50.75%.' Please correct the text or the table so the reported numbers align with the metric names.
[Sec. III-D] In the threshold analysis paragraph, 'the grayscale threshold L is between 50 and 170' should refer to T, the variable defined in Eq. (2), not L.
[Fig. 5] Figure 5 lacks axis labels and a legend; please specify which dataset and metric the IoU-versus-threshold curve corresponds to.

Circularity Check

1 steps flagged · score 4.0 of 10

Threshold T=120 is selected from the test-set IoU curve, so the headline gains are post-selection rather than independent predictions; the rest of the pipeline is not derivationally circular.

fitted input called prediction [Section III-B (Experimental setup) and Section III-D (Analysis, Fig. 5); Eq. (2)]
"In the prompts generation section, the threshold T is set to 120. ... As shown in Fig. 5, it can be seen that the IoU values are stable with the values of generally around 50% when the grayscale threshold L is between 50 and 170. The optimal value is approximately 120, indicating that this threshold can achieve good results without requiring careful design."

The paper defines no validation split: Section III-A says the Hong Kong and Turkey datasets use image-level labels for training and pixel-level labels only for testing. The IoU-versus-T curve in Fig. 5 must therefore be computed on the same test ground truth used for Table I. Choosing T=120 as the optimal point and then reporting the 'Ours' rows in Table I means the headline F1/IoU gains are conditioned on a hyperparameter selected by maximizing IoU on the evaluation set. This turns the reported margins into post-hoc fitted values rather than independent predictions. The claimed 'at least 3.0% F1 / 3.69% IoU' improvement is thus not a clean prediction, though the core pipeline (CAM -> prompts -> frozen SAM -> ResUNet) is not itself self-referential.

full rationale

Most of the paper is an empirical pipeline evaluated against baselines, not a derivation: Eq. (1) is standard CAM, Eqs. (2)-(8) are deterministic contour/centroid computations from a binarized heatmap, and Eq. (9) is frozen SAM inference. No equation is defined in terms of the final F1/IoU, and no load-bearing self-citation or imported uniqueness theorem forces the result. The main circularity concern is confined to the evaluation protocol: T=120 is selected from Fig. 5's IoU curve, which, given the reported data split, is a test-set fit; the final table inherits that selection. The wide plateau between T=50 and 170 mitigates the damage, but the absence of a validation split and of repeated runs/error bars means the specific headline margins are not established as independent predictions. The abstract's 'at least 3.0%' wording is also inconsistent with Table I's Hong Kong gains of 0.52 F1 and 0.70 IoU over the strongest baseline, but that is a reporting/correctness issue, not circularity. Overall: no derivation-level circularity; one fitted hyperparameter presented as part of the prediction, so a moderate score of 4 is appropriate.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The method introduces no new physical or architectural entities; its working parts are known components (CAM, SAM, OpenCV contour analysis). The main unstated charges are the fixed threshold T, the reliance on CAM localization quality, and the assumption that SAM's zero-shot behavior transfers to landslide imagery.

free parameters (1)

Binarization threshold T = 120
Chosen after scanning IoU versus threshold (Fig. 5) and picking the value that maximized IoU; this makes the headline results partly fitted on the evaluation data.

assumptions (3)

domain assumption Image-level labels are sufficient to learn discriminative CAMs that spatially localize landslide objects
The whole pipeline assumes the CAM from a classifier trained on image-level labels has enough spatial fidelity; invoked in Section II-A and the binarization in Eq. (2).
domain assumption Frozen SAM generalizes to 0.5-0.59 m resolution remote sensing landslide imagery with box and point prompts
APSAM relies on SAM's zero-shot segmentation; no fine-tuning is performed. Invoked in Section II-C.
ad hoc to paper A single global threshold T=120 and contour-based centroid/box extraction produce one meaningful prompt per landslide object
Eqs. (2)-(8) define the prompt generation; no size/confidence filtering or per-image adaptive threshold is used, and T is tuned on the evaluation data.

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Cite this review

Pith. "Pith review of Auto-Prompting SAM for Weakly Supervised Landslide Extraction." pith.science (2026). https://pith.science/paper/YSS5O6JK

@misc{pith2026250113426,
  author       = {Pith},
  title        = {Pith review of: Auto-Prompting SAM for Weakly Supervised Landslide Extraction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YSS5O6JK}},
  note         = {Machine review of arXiv:2501.13426}
}

read the original abstract

Weakly supervised landslide extraction aims to identify landslide regions from remote sensing data using models trained with weak labels, particularly image-level labels. However, it is often challenged by the imprecise boundaries of the extracted objects due to the lack of pixel-wise supervision and the properties of landslide objects. To tackle these issues, we propose a simple yet effective method by auto-prompting the Segment Anything Model (SAM), i.e., APSAM. Instead of depending on high-quality class activation maps (CAMs) for pseudo-labeling or fine-tuning SAM, our method directly yields fine-grained segmentation masks from SAM inference through prompt engineering. Specifically, it adaptively generates hybrid prompts from the CAMs obtained by an object localization network. To provide sufficient information for SAM prompting, an adaptive prompt generation (APG) algorithm is designed to fully leverage the visual patterns of CAMs, enabling the efficient generation of pseudo-masks for landslide extraction. These informative prompts are able to identify the extent of landslide areas (box prompts) and denote the centers of landslide objects (point prompts), guiding SAM in landslide segmentation. Experimental results on high-resolution aerial and satellite datasets demonstrate the effectiveness of our method, achieving improvements of at least 3.0\% in F1 score and 3.69\% in IoU compared to other state-of-the-art methods. The source codes and datasets will be available at https://github.com/zxk688.

Figures

Figures reproduced from arXiv: 2501.13426 by the authors.

**Figure 2.** Landslide extraction results on the Hong Kong dataset. (a) Original [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Landslide extraction results on the Turkey dataset. (a) Original image. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Comparison of prompting results obtained by SAM and APSAM. (a) [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

**Figure 5.** Figure 5: Parameter analysis of the threshold T. Table II illustrates different prompt comparison experiments conducted on the Turkey and Hong Kong datasets. We can see that, for point and box prompts, this method achieved an accuracy of 97.66%, indicating that it effectively ut…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
cs.CV 2025-07 conditional novelty 2.0 of 10

A structured survey of prompt engineering methods for the Segment Anything Model, covering geometric, textual, and multimodal prompts and their applications.

Reference graph

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Pith tools

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X. Zhang, W. Yu, X. Ma, and X. Kang, “Weakly supervised local– global anchor guidance network for landslide extraction with image-level annotations,” IEEE Geoscience and Remote Sensing Letters , vol. 20, pp. 1–5, 2023

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Segment anything,

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