EcoCropsAID: Economic Crops Aerial Image Dataset for Land Use Classification

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 explore novel approaches, such as extracting spatial and temporal features, developing deep learning architectures, and implementing transformer-based models. The EcoCropsAID dataset provides a valuable platform for advancing research in land use classification, with implications for optimizing agricultural practices and enhancing sustainable development. This study explicitly investigates the use of deep learning algorithms to classify economic crop areas in northeastern Thailand, utilizing satellite imagery to address the challenges posed by diverse patterns and similarities across categories.