Extracting Built Environment Features for Planning Research with Computer Vision: A Review and Discussion of State-of-the-Art Approaches
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Abstract (arXiv)
This is an extended abstract for a presentation at The 17th International Conference on CUPUM - Computational Urban Planning and Urban Management in June 2021. This study presents an interdisciplinary synthesis of the state-of-the-art approaches in computer vision technologies to extract built environment features that could improve the robustness of empirical research in planning. We discussed the findings from the review of studies in both planning and computer science.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 30 Jan 2022.
Builds on
8 of 32 references.
References · 8
- 2016OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networkscited by 109
- 2018Tile2Vec: Unsupervised representation learning for spatially distributed datacited by 109
- 2020Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embeddingcited by 42
- 2020ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagerycited by 27
- 2017Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scalecited by 23
- 2018Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagerycited by 22
- 2020OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagerycited by 9
- 2020Holistic Multi-View Building Analysis in the Wild with Projection Poolingcited by 5
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