2026· Journal of image processing theory and applications· 0 citations· 20 references
TL;DR
The first systematic zero-shot evaluation of SAM 2 for aerial building segmentation is presented, establishing SAM 2 as a viable tool for rapid building mapping while highlighting where domain adaptation remains necessary.
Abstract
: Building footprint extraction from remote sensing imagery underpins urban planning, population estimation, and disaster damage assessment. Deep learning methods have achieved high accuracy for this task, but their dependence on large-scale pixel-level annotations creates a severe bottleneck: annotating a city-scale dataset demands hundreds of hours of manual labor, limiting rapid deployment to new regions. The Segment Anything Model 2 (SAM 2), a foundation model with zero-shot segmentation capability, offers a potential solution by eliminating the need for task-specific annotations entirely. Yet SAM 2 was trained exclusively on natural scene images and videos, raising a critical question: can it generalize to the fundamentally different visual characteristics of aerial remote sensing imagery? This paper presents the first systematic zero-shot evaluation of SAM 2 for aerial building segmentation. We conduct three groups of experiments: (1) benchmarking four SAM 2 model variants to identify the optimal accuracy-efficiency trade-off; (2) stratified evaluation across dense, sparse, large-scale, and small-scale building morphologies to reveal which architectural characteristics challenge SAM 2 most; and (3) comparison of single-point, multi-point, and bounding-box prompting strategies to derive practical guidelines. Results demonstrate that SAM 2 Base+ achieves an IoU of 0.7738 without any training data, while oracle bounding-box prompting reaches 0.8755. SAM 2 excels on dense and large-scale buildings but struggles with sparse scenes. These findings establish SAM 2 as a viable tool for rapid building mapping while highlighting where domain adaptation remains necessary.
The automated detection and classification of surface degradation on historical buildings represents a critical challenge in architectural heritage conservation. Conventional approaches relying on manual inspection or supervised machine learning require extensive annotated datasets and expert involvement, limiting their scalability. This paper presents a novel zero-shot pipeline for degradation segmentation on historical civil architecture, combining UAV-acquired photogrammetric data processed in Agisoft Metashape with Gemma 4 31B, Google DeepMind's flagship open-weight vision language model, running locally via LM Studio, and the Segment Anything Model 2 (SAM2) for pixel-accurate mask generation. The system operates entirely without task-specific training data, producing segmentation masks overlaid on the RGB orthomosaic for expert visual evaluation. A case study on a degraded historical building in Calabria, southern Italy, demonstrates the pipeline's ability to detect and categorize detachment, cracking, and lacunae in a unified, reproducible workflow. Results are evaluated through structured expert visual assessment. The approach offers a replicable, low-cost alternative to supervised segmentation, particularly suited to contexts where labeled data is unavailable.
Francesco Demarco, Federico De Francesca, Pierpaolo Antonio Fusaro et al.· 2026 IEEE International Work...· 0 citations
The results establish zero-shot NAS as a computationally efficient paradigm for large-scale Earth observation segmentation as a training-free strategy for semantic segmentation in Earth observation.
Gabriel Iuhasz, Marian Neagul· IEEE Access· 0 citations
In the present generation of increasing geospatial data, accurate and automated extraction of building footprints from high-resolution aerial and satellite imagery has become crucial for various applications such as urban planning, infrastructure development, disaster management, and GIS database maintenance, as manual tracing is time-consuming and unstable for large-scale mapping. This study compares conventional image processing techniques such as thresholding, edge detection, morphological operations through a machine learning approach using Random Forest (RF), and deep learning-based semantic segmentation models, namely U-Net and DeepLabV3+, along with the Segment Anything Model (SAM) using a pre-trained prompt-based setup. All methods are tested on the same set of data, and a standardized data preprocessing is performed for fair comparison. The overall results indicate that the application of DeepLabV3+ is best, with an IoU of 82% and an F1 score of 90%. U-Net achieves second high IoU and F1 scores of 74% and 84% respectively, while Random Forest shows a high IoU of 60% and an F1-score of 72%. SAM has the lowest scores with an IoU of 50% and an F1 score of 51%.
Accurate extraction of building footprints from remote sensing imagery is important for urban planning, disaster management, and geographic information systems. However, complex building shapes, occlusions, and scale variation continue to challenge conventional segmentation models. This paper presents SRB-Net, a U-Net-based framework that combines three complementary components: (1) strip pooling (SP) for long-range horizontal and vertical context; (2) residual multi-scale atrous spatial pyramid pooling (RMASPP) with squeeze-and-excitation (SE) blocks for multi-scale and channel-aware feature learning; and (3) a bottleneck attention module (BAM) for refining skip-connection features. The model was trained with the Adam optimizer and evaluated on the aerial and Satellite Dataset II subsets of the WHU Building Dataset. Among the evaluated baselines, SRB-Net achieved the best overall performance, reaching 98.83% accuracy and 90.12% Intersection over Union (IoU) on the aerial dataset and 98.28% accuracy and 70.89% IoU on Satellite Dataset II. These results show consistent performance improvements across the two evaluated WHU subsets while avoiding claims beyond the within-dataset experimental setting.
Hamdoun Youssef, Xingyuan Li, Yongtao Yu et al.· Journal of Computing and Ele...· 0 citations
This study proposes a zero-shot burned area mapping approach based on the Segment Anything Model (SAM) using Sentinel-2 data and demonstrates that SAM can serve as a powerful, scalable, and low-cost framework for zero-shot environmental monitoring and automatic burned area detection, particularly in data-scarce or time-critical post-fire assessment scenarios.
Domain-specific fine-tuning, coupled with the proposed PPA and NPC frameworks, successfully mitigates the limitations of SAM 2 in agricultural remote sensing and provides a robust methodology for automated, high-precision land segmentation.
Yayang Setia Budi, Fardan Al Jihad, Nurjannah Syakrani et al.· Journal of Information Syste...· 0 citations