Segment Any Building

TL;DR

This paper introduces a novel building segmentation method that leverages multiple datasets and advanced representation learning, achieving superior performance and setting new benchmarks in remote sensing imagery analysis.

Contribution

The study presents a new approach combining dataset fusion and pre-trained models to significantly improve building segmentation accuracy.

Findings

Achieved state-of-the-art performance across multiple datasets.

Demonstrated the effectiveness of dataset amalgamation for model training.

Set new benchmarks in building segmentation tasks.

Abstract

The task of identifying and segmenting buildings within remote sensing imagery has perennially stood at the forefront of scholarly investigations. This manuscript accentuates the potency of harnessing diversified datasets in tandem with cutting-edge representation learning paradigms for building segmentation in such images. Through the strategic amalgamation of disparate datasets, we have not only expanded the informational horizon accessible for model training but also manifested unparalleled performance metrics across multiple datasets. Our avant-garde joint training regimen underscores the merit of our approach, bearing significant implications in pivotal domains such as urban infrastructural development, disaster mitigation strategies, and ecological surveillance. Our methodology, predicated upon the fusion of datasets and gleaning insights from pre-trained models, carves a new benchmark in the annals of building segmentation endeavors. The outcomes of this research both fortify the foundations for ensuing scholarly pursuits and presage a horizon replete with innovative applications in the discipline of building segmentation.