5M-Building: A Large-Scale High-Resolution Building Dataset with CNN Based Detection Analysis

Abstract

Building detection in remote sensing images plays an important role in applications such as urban management and urban planning. Recently, convolutional neural network (CNN) based methods which benefits from the popularity of large-scale datasets have achieved good performance for object detection. To our best knowledge, there is no large-scale remote sensing image dataset specially build for building detection. Existing building datasets are in small size and lack of diversity, which hinder the development of building detection. In this paper, we present a large-scale high-resolution building dataset named 5M-Building after the number of samples in the dataset. The dataset consists of more than 10 thousand images all collected from GaoFen-2 with a spatial resolution of 0.8 meter. We also present a baseline for the dataset by evaluating three state of the art CNN based detectors. The experiments demonstrate that it is great challenge to accurately detect various buildings from remote sensing images. We hope the 5M-Building dataset will facilitate the research on building detection.