Building height estimation using Google EarthBuilding heightSatellite imageGoogle EarthShadow analysisIn order to provide geometric information for the research of the relationship between a building's shape and its energy consumption, we propose a method to estimate building height by Google Earth. The...
Building and shadow instances were first extracted using a U-Net deep learning framework. Based on the building height annotations retrieved from ICESat-2 photons, an improved shadow-based building height estimation model by minimizing the global error across all sample buildings was developed. A ...
mid-rise (10–24 m), and high-rise buildings (>24 m), allowing for a more thorough examination of the model’s estimation for multiple building height hierarchies. As shown in Fig.5, both the spatially-explicit model (RMSE reduced by 2.16 m) ...
We created global building height samples by aggregating filtered high-quality GEDI footprints into a 150-m grid size. However, only using single relative height metrics may cause an overestimation of the residential areas consisting of low-rise single-family house buildings surrounded by tall trees26...
Building height estimation using Google Earth. Energy Build. 2016, 118, 123–132. [Google Scholar] [CrossRef] Liu, C.; Huang, X.; Wen, D.; Chen, H.; Gong, J. Assessing the quality of building height extraction from ZiYuan-3 multi-view imagery. Remote Sens. Lett. 2017, 8, 907–...
Still, for the building class, where height data is indeed highly relevant, α=0.5 yields the best F1 score, also outperforming U-Net. For that reason, U-Net is not further utilized. The Munich Dataset has quite different radiometric properties and only half the resolution of Potsdam. For ...
& Zhao, Y. Satellite scatterometer estimation of urban built-up volume: validation with airborne lidar data. Int. J. Appl. Earth Obs. Geoinform. 77, 100–107 (2019). Google Scholar Frolking, S., Mahtta, R., Milliman, T. & Seto, K. C. Three decades of global trends in urban ...
Article Google Scholar Soergel U, Michaelsen E, Thiele A, Cadario E, Thoennessen U: Stereo analysis of high-resolution SAR images for building height estimation in cases of orthogonal aspect directions. ISPRS J Photogramm Remote Sens 2009, 64: 490-500. 10.1016/j.isprsjprs.2008.10.007 Article...
A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities 2021, Remote Sensing of Environment Citation Excerpt : The “predict-then-aggregate” strategy adopted by the M3Net can better exploit the spatial deta...
Therefore, some studies have deeply explored the coupling mechanisms between SAR data and building height using machine learning approaches, thus establishing urban building height estimation models. Spatially continuous inversion of building heights has been achieved at various spatial resolutions ranging ...