Chao Ding - Academia.edu (original) (raw)
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Papers by Chao Ding
Urban areas consume over two-thirds of the world’s energy and account for more than 70% of global... more Urban areas consume over two-thirds of the world’s energy and account for more than 70% of global CO2 emissions. As stated in IPCC’s Global Warming of 1.5 oC report, achieving carbon neutrality by 2050 requires a scalable approach that can be applied in a global context. Conventional methods of collecting data on energy use and emissions of buildings are extremely expensive and require specialized geometry information that not all cities have readily available. High-quality building footprint generation from satellite images can accelerate this predictive process and empower municipal decision-making at scale. However, previous deep learning-based approaches use supplemental data such as point cloud data, building height information, and multi-band imagery which has limited availability and is difficult to produce. In this paper, we propose a modified DeeplabV3+ module with a Dilated ResNet backbone to generate masks of building footprints from only three-channel RGB satellite image...
Urban areas consume over two-thirds of the world’s energy and account for more than 70% of global... more Urban areas consume over two-thirds of the world’s energy and account for more than 70% of global CO2 emissions. As stated in IPCC’s Global Warming of 1.5 oC report, achieving carbon neutrality by 2050 requires a scalable approach that can be applied in a global context. Conventional methods of collecting data on energy use and emissions of buildings are extremely expensive and require specialized geometry information that not all cities have readily available. High-quality building footprint generation from satellite images can accelerate this predictive process and empower municipal decision-making at scale. However, previous deep learning-based approaches use supplemental data such as point cloud data, building height information, and multi-band imagery which has limited availability and is difficult to produce. In this paper, we propose a modified DeeplabV3+ module with a Dilated ResNet backbone to generate masks of building footprints from only three-channel RGB satellite image...