# IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling

arXiv: https://arxiv.org/abs/2112.09126 · HTML: https://mlsanity.com/paper/2112.09126/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Using satellite imagery to understand and promote sustainable development](https://mlsanity.com/paper/2010.06988/) (2020)
- [Continental-Scale Building Detection from High Resolution Satellite Imagery](https://mlsanity.com/paper/2107.12283/) (2021)
- [Learning When and Where to Zoom with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.00425/) (2020)
- [Generating Interpretable Poverty Maps using Object Detection in Satellite Images](https://mlsanity.com/paper/2002.01612/) (2020)
- [Efficient Object Detection in Large Images using Deep Reinforcement Learning](https://mlsanity.com/paper/1912.03966/) (2019)
- [Learning to Interpret Satellite Images Using Wikipedia](https://mlsanity.com/paper/1809.10236/) (2018)
- [Predicting Economic Development using Geolocated Wikipedia Articles](https://mlsanity.com/paper/1905.01627/) (2019)
- [Counting dense objects in remote sensing images](https://mlsanity.com/paper/2002.05928/) (2020)
- [Estimation of Regional Economic Development Indicator from Transportation Network Analytics](https://mlsanity.com/paper/2002.00566/) (2020)

## Built on by (most cited first, 14 of 14)

- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [RGB-D Indiscernible Object Counting in Underwater Scenes](https://mlsanity.com/paper/2304.11677/) (2023)
- [A Visual Active Search Framework for Geospatial Exploration](https://mlsanity.com/paper/2211.15788/) (2022)
- [A Partially Supervised Reinforcement Learning Framework for Visual Active Search](https://mlsanity.com/paper/2310.09689/) (2023)
- [InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition](https://mlsanity.com/paper/2505.15818/) (2025)
- [DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling](https://mlsanity.com/paper/2306.03151/) (2023)
- [Human-in-the-Loop Visual Re-ID for Population Size Estimation](https://mlsanity.com/paper/2312.05287/) (2023)
- [Active Measurement: Efficient Estimation at Scale](https://mlsanity.com/paper/2507.01372/) (2025)
- [Scale-Aware Recognition in Satellite Images under Resource Constraints](https://mlsanity.com/paper/2411.00210/) (2024)
- [Building Coverage Estimation with Low-resolution Remote Sensing Imagery](https://mlsanity.com/paper/2301.01449/) (2023)
- [Importance Sampling for Nonlinear Models](https://mlsanity.com/paper/2505.12353/) (2025)
- [Active Measurement of Two-Point Correlations](https://mlsanity.com/paper/2604.05227/) (2026)
- [DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments](https://mlsanity.com/paper/2605.15519/) (2026)
- [Scalable Model-Assisted Multi-Target Estimation in Large Image Collections](https://mlsanity.com/paper/2607.17581/) (2026)

## Abstract (arXiv)

Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitoring applications. However, performing object detection over large geographies can still be prohibitively expensive due to the high cost of purchasing imagery and compute. Inspired by traditional survey data collection strategies, we propose an approach to estimate object count statistics over large geographies through sampling. Given a cost budget, our method selects a small number of representative areas by sampling from a learnable proposal distribution. Using importance sampling, we are able to accurately estimate object counts after processing only a small fraction of the images compared to an exhaustive approach. We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U.S., while requiring as few as 0.01% of satellite images compared to an exhaustive approach.