# Land Use Classification in Remote Sensing Images by Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1508.00092 · HTML: https://mlsanity.com/paper/1508.00092/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2015.

## Built on by (most cited first, 19 of 19)

- [From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data](https://mlsanity.com/paper/2205.03147/) (2022)
- [Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification](https://mlsanity.com/paper/2207.07189/) (2022)
- [FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning](https://mlsanity.com/paper/2303.07035/) (2023)
- [Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector](https://mlsanity.com/paper/2112.01723/) (2021)
- [Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2302.01793/) (2023)
- [Relict landslide detection using Deep-Learning architectures for image segmentation in rainforest areas: A new framework](https://mlsanity.com/paper/2208.02693/) (2022)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [Airplane Detection Based on Mask Region Convolution Neural Network](https://mlsanity.com/paper/2108.12817/) (2021)
- [Multi-Label Classification on Remote-Sensing Images](https://mlsanity.com/paper/2201.01971/) (2022)
- [Forensic Dental Age Estimation Using Modified Deep Learning Neural Network](https://mlsanity.com/paper/2208.09799/) (2022)
- [Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models](https://mlsanity.com/paper/2211.11797/) (2022)
- [Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery](https://mlsanity.com/paper/2304.09874/) (2023)
- [On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing](https://mlsanity.com/paper/2501.11462/) (2025)
- [A Hierarchical Approach to Remote Sensing Scene Classification](https://mlsanity.com/paper/2103.15463/) (2021)
- [DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification](https://mlsanity.com/paper/2212.10411/) (2022)
- [Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification](https://mlsanity.com/paper/2303.11389/) (2023)
- [The identification of garbage dumps in the rural areas of Cyprus through the application of deep learning to satellite imagery](https://mlsanity.com/paper/2308.02502/) (2023)
- [Practical GPU Choices for Earth Observation: ResNet-50 Training Throughput on Integrated, Laptop, and Cloud Accelerators](https://mlsanity.com/paper/2508.18206/) (2025)
- [Hierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2510.27155/) (2025)

## Abstract (arXiv)

We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that are only fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on two remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which guarantees a significant performance improvement over all state-of-the-art references.