用深度学习精准区分住宅与非住宅建筑，助力城市规划决策。
Classification of residential and non-residential buildings based on satellite data using deep learning
- 融合高分辨率卫星影像与地形数据，结合特征工程优化模型。
- 在大规模数据上实现0.9936的总体F1分数，分类性能优异。
- 适合城市规划、资源分配等需要建筑类型分析的场景使用。
将建筑物准确分类为住宅与非住宅类别对城市规划、基础设施建设、人口估算和资源分配至关重要。手动利用卫星数据进行建筑自动分类是一项复杂任务。本文提出一种新型深度学习方法，结合50厘米分辨率影像与1米网格间隔的数字高程模型（DEM）及矢量数据，实现高性能建筑分类。模型采用LeakyReLU与ReLU激活函数捕捉数据非线性特征，并通过特征工程消除高度相关特征，提升计算效率。在大规模数据集上的实验表明，该模型整体F1分数达到0.9936，验证了其有效性。所提方法为建筑分类提供了一种可扩展、高精度的解决方案，支持城市规划与资源分配中的科学决策，推动了城市分析领域的发展。
原文摘要 · Abstract (English)
Accurate classification of buildings into residential and non-residential categories is crucial for urban planning, infrastructure development, population estimation and resource allocation. It is a complex job to carry out automatic classification of residential and nonresidential buildings manually using satellite data. In this paper, we are proposing a novel deep learning approach that combines high-resolution satellite data (50 cm resolution Image + 1m grid interval DEM) and vector data to achieve high-performance building classification. Our architecture leverages LeakyReLU and ReLU activations to capture nonlinearities in the data and employs feature-engineering techniques to eliminate highly correlated features, resulting in improved computational efficiency. Experimental results on a large-scale dataset demonstrate the effectiveness of our model, achieving an impressive overall F1 -score of 0.9936. The proposed approach offers a scalable and accurate solution for building classification, enabling informed decision-making in urban planning and resource allocation. This research contributes to the field of urban analysis by providing a valuable tool for understanding the built environment and optimizing resource utilization.
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