2026-10-13T06:43:01Z https://www.scidb.cn/oai

10.57760/sciencedb.011u2 2026-09-09T15:28:56Z

2026-09-09 GSS²USW (Global-South Satellite&Street-view images for Urban Solid Waste classification) doi:10.57760/sciencedb.011u2 en We provide a spatially aligned multi-modal benchmark dataset specifically designed for identification of Urban Solid Waste (USW) in typical cities of the Global South, by integrating satellite remote sensing and panoramic street-view imagery, i.e., GSS2USW. Specifically, it covers Jakarta (Indonesia), Manila (Philippines), Dar es Salaam (Tanzania), Johannesburg (South Africa), Nakuru (Kenya), Medellín (Colombia), Belo Horizonte (Brazil), Santiago (Chile), and Buenos Aires (Argentina).Countries in the Global South commonly face governance challenges such as dense informal settlements, fragmented urban landscapes and severe illegal solid waste dumping. Nadir view from satellite remote sensing is highly susceptible to interference from tree occlusion and building shadows, while street-view imagery suffers from limited field-of-view and lacks macro-level scene context. To address these limitations, this dataset leverages the geographic coordinates of street-view sampling points as a linking anchor, and systematically constructs 5,145 spatially aligned multi-modal satellite and street-view samples, each consisting of a very-high-resolution satellite image patch and ground-level panoramic street view images. It aims to provide data support for low-cost, high-accuracy urban solid waste monitoring and multi-modal data fusion and computing. Global South; Urban Solid Waste; satellite remote sensing; street-view imagery Cui Rongji Feng Quanlong Niu Bowen Yuan Shiying Jiang Zihang Yu Jiaqi Yin Shoujing Li Ying PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank