# Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition

Source: [Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition](<https://www.sota2.com/research/paper/68fa636e1e209131ce9febe0>)

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

Data updated: 2025-10-25T20:41:16.530Z

Authors: Vladimir Iglovikov, Sergey Mushinskiy, Vladimir Osin

Published: 2017-06-19T20:41:42.000Z

## Abstract

This paper describes our approach to the DSTL Satellite Imagery Feature Detection challenge run by Kaggle. The primary goal of this challenge is accurate semantic segmentation of different classes in satellite imagery. Our approach is based on an adaptation of fully convolutional neural network for multispectral data processing. In addition, we defined several modifications to the training objective and overall training pipeline, e.g. boundary effect estimation, also we discuss usage of data augmentation strategies and reflectance indices. Our solution scored third place out of 419 entries. Its accuracy is comparable to the first two places, but unlike those solutions, it doesn't rely on complex ensembling techniques and thus can be easily scaled for deployment in production as a part of automatic feature labeling systems for satellite imagery analysis.

## 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/1706.06169>)

[PDF](<https://arxiv.org/pdf/1706.06169.pdf>)