Zero-Shot Refinement of Buildings' Segmentation Models using SAM

• URL: http://arxiv.org/abs/2310.01845v2
• Date: Sun, 11 Feb 2024 14:28:09 GMT
• Title: Zero-Shot Refinement of Buildings' Segmentation Models using SAM
• Authors: Ali Mayladan, Hasan Nasrallah, Hasan Moughnieh, Mustafa Shukor and Ali J. Ghandour
• Abstract summary: We present a novel approach to adapt foundation models to address existing models' generalization dropback. Among several models, our focus centers on the Segment Anything Model (SAM) SAM does not offer recognition abilities and thus fails to classify and tag localized objects. This novel approach augments SAM with recognition abilities, a first of its kind.
• Score: 6.110856077714895
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Foundation models have excelled in various tasks but are often evaluated on general benchmarks. The adaptation of these models for specific domains, such as remote sensing imagery, remains an underexplored area. In remote sensing, precise building instance segmentation is vital for applications like urban planning. While Convolutional Neural Networks (CNNs) perform well, their generalization can be limited. For this aim, we present a novel approach to adapt foundation models to address existing models' generalization dropback. Among several models, our focus centers on the Segment Anything Model (SAM), a potent foundation model renowned for its prowess in class-agnostic image segmentation capabilities. We start by identifying the limitations of SAM, revealing its suboptimal performance when applied to remote sensing imagery. Moreover, SAM does not offer recognition abilities and thus fails to classify and tag localized objects. To address these limitations, we introduce different prompting strategies, including integrating a pre-trained CNN as a prompt generator. This novel approach augments SAM with recognition abilities, a first of its kind. We evaluated our method on three remote sensing datasets, including the WHU Buildings dataset, the Massachusetts Buildings dataset, and the AICrowd Mapping Challenge. For out-of-distribution performance on the WHU dataset, we achieve a 5.47\% increase in IoU and a 4.81\% improvement in F1-score. For in-distribution performance on the WHU dataset, we observe a 2.72\% and 1.58\% increase in True-Positive-IoU and True-Positive-F1 score, respectively. Our code is publicly available at this Repo (https://github.com/geoaigroup/GEOAI-ECRS2023), hoping to inspire further exploration of foundation models for domain-specific tasks within the remote sensing community.

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