An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images

Abstract

Accurately segmenting building roofs from satellite images is crucial for evaluating the photovoltaic power generation potential of urban roofs and is a worthwhile research topic. In this study, we propose an attention-based full-scale fusion (AFSF) network to segment a roof mask from the given satellite images. By developing an attention-based residual ublock, the channel relationship of the feature maps can be modeled. By integrating attention mechanisms in multi-scale feature fusion, the model can learn different weights for features of different scales. We also design a ladder-like network to utilize weakly labeled data, thereby achieving pixel-level semantic segmentation tasks assisted by image-level classification tasks. In addition, we contribute a new roof segmentation dataset, which is based on satellite images and uses the roof as the segmentation target rather than the entire building to further promote the algorithm research of estimating roof area using satellite images. The experimental results on the new roof segmentation dataset, WHU dataset, and IAIL dataset demonstrate the effectiveness of the proposed network.

Advancing Spatiotemporal Pollutant Dispersion Forecasting with an Integrated Deep Learning Framework for Crucial Information Capture

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