# Cross-Region Building Counting in Satellite Imagery using Counting Consistency

arXiv: https://arxiv.org/abs/2110.13558 · HTML: https://mlsanity.com/paper/2110.13558/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 64 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 30 of 31)

- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [Context-Aware Crowd Counting](https://mlsanity.com/paper/1811.10452/) (2018)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Switching Convolutional Neural Network for Crowd Counting](https://mlsanity.com/paper/1708.00199/) (2017)
- [Mean Absolute Percentage Error for regression models](https://mlsanity.com/paper/1605.02541/) (2016)
- [Leveraging Unlabeled Data for Crowd Counting by Learning to Rank](https://mlsanity.com/paper/1803.03095/) (2018)
- [Distributed Clustering of Linear Bandits in Peer to Peer Networks](https://mlsanity.com/paper/1604.07706/) (2016)
- [Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2007.14449/) (2020)
- [Curriculum Self-Paced Learning for Cross-Domain Object Detection](https://mlsanity.com/paper/1911.06849/) (2019)
- [Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images](https://mlsanity.com/paper/1905.03198/) (2019)
- [Spatiotemporal Modeling for Crowd Counting in Videos](https://mlsanity.com/paper/1707.07890/) (2017)
- [FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras](https://mlsanity.com/paper/1707.09476/) (2017)
- [ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks](https://mlsanity.com/paper/1907.12859/) (2019)
- [People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting](https://mlsanity.com/paper/1711.05586/) (2017)
- [MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling](https://mlsanity.com/paper/1909.13776/) (2019)
- [A better measure of relative prediction accuracy for model selection and model estimation](https://mlsanity.com/paper/2105.05249/) (2021)
- [Fast Distributed Bandits for Online Recommendation Systems](https://mlsanity.com/paper/2007.08061/) (2020)
- [Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion](https://mlsanity.com/paper/1804.00307/) (2018)
- [Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid](https://mlsanity.com/paper/1805.06115/) (2018)
- [Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks](https://mlsanity.com/paper/2004.14010/) (2020)
- [CODA: Counting Objects via Scale-aware Adversarial Density Adaption](https://mlsanity.com/paper/1903.10442/) (2019)
- [Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery](https://mlsanity.com/paper/2007.02277/) (2020)
- [Cross-regional oil palm tree counting and detection via multi-level attention domain adaptation network](https://mlsanity.com/paper/2008.11505/) (2020)
- [Deep Built-Structure Counting in Satellite Imagery Using Attention Based Re-Weighting](https://mlsanity.com/paper/1904.00674/) (2019)
- [GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images](https://mlsanity.com/paper/1811.02793/) (2018)
- [Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data](https://mlsanity.com/paper/2102.00848/) (2021)

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

- [Centerpoints Are All You Need in Overhead Imagery](https://mlsanity.com/paper/2210.01857/) (2022)
- [Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagery](https://mlsanity.com/paper/2309.15625/) (2023)

## Abstract (arXiv)

Estimating the number of buildings in any geographical region is a vital component of urban analysis, disaster management, and public policy decision. Deep learning methods for building localization and counting in satellite imagery, can serve as a viable and cheap alternative. However, these algorithms suffer performance degradation when applied to the regions on which they have not been trained. Current large datasets mostly cover the developed regions and collecting such datasets for every region is a costly, time-consuming, and difficult endeavor. In this paper, we propose an unsupervised domain adaptation method for counting buildings where we use a labeled source domain (developed regions) and adapt the trained model on an unlabeled target domain (developing regions). We initially align distribution maps across domains by aligning the output space distribution through adversarial loss. We then exploit counting consistency constraints, within-image count consistency, and across-image count consistency, to decrease the domain shift. Within-image consistency enforces that building count in the whole image should be greater than or equal to count in any of its sub-image. Across-image consistency constraint enforces that if an image contains considerably more buildings than the other image, then their sub-images shall also have the same order. These two constraints encourage the behavior to be consistent across and within the images, regardless of the scale. To evaluate the performance of our proposed approach, we collected and annotated a large-scale dataset consisting of challenging South Asian regions having higher building densities and irregular structures as compared to existing datasets. We perform extensive experiments to verify the efficacy of our approach and report improvements of approximately 7% to 20% over the competitive baseline methods.