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

arXiv: https://arxiv.org/abs/1706.06169 · HTML: https://mlsanity.com/paper/1706.06169/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 10 of 10)

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- [Segmenting across places: The need for fair transfer learning with satellite imagery](https://mlsanity.com/paper/2204.04358/) (2022)
- [Efficacy of BERT embeddings on predicting disaster from Twitter data](https://mlsanity.com/paper/2108.10698/) (2021)
- [Financial Table Extraction in Image Documents](https://mlsanity.com/paper/2405.05260/) (2024)
- [GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery](https://mlsanity.com/paper/2101.06390/) (2021)
- [Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities](https://mlsanity.com/paper/2206.13677/) (2022)
- [Synthetic Data Matters: Re-training with Geo-typical Synthetic Labels for Building Detection](https://mlsanity.com/paper/2507.16657/) (2025)
- [Buildings Classification using Very High Resolution Satellite Imagery](https://mlsanity.com/paper/2111.14650/) (2021)
- [Flaws in the LLM Automation Narrative](https://mlsanity.com/paper/2606.11166/) (2026)

## Abstract (arXiv)

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.