# Urban land-use analysis using proximate sensing imagery: a survey

arXiv: https://arxiv.org/abs/2101.04827 · HTML: https://mlsanity.com/paper/2101.04827/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 98 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives](https://mlsanity.com/paper/1407.0202/) (2014)
- [Places: An Image Database for Deep Scene Understanding](https://mlsanity.com/paper/1610.02055/) (2016)
- [Cross-View Image Matching for Geo-localization in Urban Environments](https://mlsanity.com/paper/1703.07815/) (2017)
- [Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks](https://mlsanity.com/paper/1509.06041/) (2015)
- [Building Instance Classification Using Street View Images](https://mlsanity.com/paper/1802.09026/) (2018)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution](https://mlsanity.com/paper/1905.01752/) (2019)
- [PixelNet: Representation of the pixels, by the pixels, and for the pixels](https://mlsanity.com/paper/1702.06506/) (2017)
- [A Unified Model for Near and Remote Sensing](https://mlsanity.com/paper/1708.03035/) (2017)
- [Correcting rural building annotations in OpenStreetMap using convolutional neural networks](https://mlsanity.com/paper/1901.08190/) (2019)
- [Towards seamless multi-view scene analysis from satellite to street-level](https://mlsanity.com/paper/1705.08101/) (2017)
- [AiRound and CV-BrCT: Novel Multi-View Datasets for Scene Classification](https://mlsanity.com/paper/2008.01133/) (2020)
- [Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images](https://mlsanity.com/paper/1802.02668/) (2018)
- [Bounding Boxes Are All We Need: Street View Image Classification via Context Encoding of Detected Buildings](https://mlsanity.com/paper/2010.01305/) (2020)
- [Land Use Classification using Convolutional Neural Networks Applied to Ground-Level Images](https://mlsanity.com/paper/1609.06653/) (2016)
- [Spatial Morphing Kernel Regression For Feature Interpolation](https://mlsanity.com/paper/1802.07452/) (2018)

## Built on by (most cited first, 2 of 2)

- [Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images](https://mlsanity.com/paper/2301.00622/) (2023)
- [Unsupervised Urban Land Use Mapping with Street View Contrastive Clustering and a Geographical Prior](https://mlsanity.com/paper/2504.17551/) (2025)

## Abstract (arXiv)

Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile devices equipped with Global Positioning System (GPS) greatly proliferates proximate sensing images taken near or on the ground at a close distance to urban targets. Studies leveraging proximate sensing imagery have demonstrated great potential to address the need for local data in urban land-use analysis. This paper reviews and summarizes the state-of-the-art methods and publicly available datasets from proximate sensing to support land-use analysis. We identify several research problems in the perspective of examples to support training of models and means of integrating diverse data sets. Our discussions highlight the challenges, strategies, and opportunities faced by the existing methods using proximate sensing imagery in urban land-use studies.