# An Empirical Study of Methods for Small Object Detection from Satellite Imagery

Source: [An Empirical Study of Methods for Small Object Detection from Satellite Imagery](<https://www.sota2.com/research/paper/68fa76d31e209131cea6f532>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-02T05:00:04.167Z

Authors: Xiaohui Yuan, Aniv Chakravarty, Lichuan Gu, Zhenchun Wei, Elinor Lichtenberg, Tian Chen

Published: 2025-02-05T23:40:54.000Z

## Abstract

This paper reviews object detection methods for finding small objects from remote sensing imagery and provides an empirical evaluation of four state-of-the-art methods to gain insights into method performance and technical challenges. In particular, we use car detection from urban satellite images and bee box detection from satellite images of agricultural lands as application scenarios. Drawing from the existing surveys and literature, we identify several top-performing methods for the empirical study. Public, high-resolution satellite image datasets are used in our experiments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2502.03674>)

[PDF](<https://arxiv.org/pdf/2502.03674.pdf>)