# Object Detection in Satellite Imagery using 2-Step Convolutional Neural Networks

Source: [Object Detection in Satellite Imagery using 2-Step Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa64bf1e209131cea06403>)

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

Data updated: 2025-10-27T05:55:49.570Z

Authors: Hiroki Miyamoto, Kazuki Uehara, Masahiro Murakawa, Hidenori Sakanashi, Hirokazu Nosato, Toru Kouyama, Ryosuke Nakamura

Published: 2018-08-09T02:12:43.000Z

## Abstract

This paper presents an efficient object detection method from satellite imagery. Among a number of machine learning algorithms, we proposed a combination of two convolutional neural networks (CNN) aimed at high precision and high recall, respectively. We validated our models using golf courses as target objects. The proposed deep learning method demonstrated higher accuracy than previous object identification methods.

## 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/1808.02996>)

[PDF](<https://arxiv.org/pdf/1808.02996.pdf>)