EcoCropsAID: Economic Crops Aerial Image Dataset for Land Use Classification
Sangdaow Noppitak, Emmanuel Okafor, Olarik Surinta

TL;DR
EcoCropsAID is a diverse aerial image dataset of key Thai crops, designed to advance land use classification research through challenging variability and supporting deep learning approaches.
Contribution
The paper introduces EcoCropsAID, a comprehensive aerial image dataset with diverse crop stages and sensor variations, facilitating novel AI methods for land use classification.
Findings
Deep learning algorithms effectively classify crops using EcoCropsAID.
Variability in images presents challenges but also opportunities for robust models.
The dataset enables exploration of spatial, temporal, and transformer-based features.
Abstract
The EcoCropsAID dataset is a comprehensive collection of 5,400 aerial images captured between 2014 and 2018 using the Google Earth application. This dataset focuses on five key economic crops in Thailand: rice, sugarcane, cassava, rubber, and longan. The images were collected at various crop growth stages: early cultivation, growth, and harvest, resulting in significant variability within each category and similarities across different categories. These variations, coupled with differences in resolution, color, and contrast introduced by multiple remote imaging sensors, present substantial challenges for land use classification. The dataset is an interdisciplinary resource that spans multiple research domains, including remote sensing, geoinformatics, artificial intelligence, and computer vision. The unique features of the EcoCropsAID dataset offer opportunities for researchers to…
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Taxonomy
TopicsRemote Sensing and Land Use · Remote Sensing in Agriculture
