OliveTreeCrownsDb: A High-Resolution Dataset for Agricultural Computer Vision

Published: 22 November 2024| Version 1 | DOI: 10.17632/xym8rd2srf.1
Contributors:
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, Ali Achebour,
,
,

Description

The OliveTreeCrown dataset contains high-resolution images divided into various grid configurations: 1×1 (original), 3×3, 6×6, and 9×9. Each segment is thoroughly annotated to ensure accurate object detection, providing precise and detailed labeling of olive tree crowns. In addition to the annotated image data, the dataset contains a point cloud representation, a Digital Elevation Model (DEM), and spatial data in Keyhole Markup Language (KML) format. These components collectively capture the three-dimensional geometry, topographic features, and geospatial characteristics of the study area. The XYZ coordinates in the point cloud data define the precise spatial position of each point, contributing to a comprehensive spatial representation. By integrating 3D data and geospatial attributes, this dataset offers a valuable resource for advanced spatial modeling and analysis. It serves as a solid foundation for applications such as multi-scale analysis, 3D mapping, and precision agriculture, fostering innovation in remote sensing and AI-driven agricultural solutions.

Files

Steps to reproduce

In the Meknes region of Morocco, a DJI Phantom 4 RTK drone captured 46 high-resolution images over the study farm at an altitude of 74.6 meters. To process and organize this data for analysis, follow these steps: 1.Data Export and Segmentation: Segment each image into grids of 1×1 (original), 3×3, 6×6, and 9×9 to facilitate multi-scale analysis. 2.Metadata Extraction: Retrieve key metadata such as date, time, GPS coordinates, altitude, and weather conditions from each image to enrich the dataset with essential contextual information. 3.Generating Point Cloud, DEM, and Orthophoto: Generate a dense point cloud to capture detailed terrain features. Create a Digital Elevation Model (DEM) to represent elevation and topography. Produce an orthophoto to provide an accurate, georeferenced image of the study area, useful for spatial analysis.

Institutions

Universite Sidi Mohamed Ben Abdellah Faculte des Sciences Dhar El Mahraz-Fes

Categories

Computer Vision, Image Processing, Image Acquisition, Image Segmentation, Object Detection, 3D Analysis, Image Classification, Orthophoto, Point Cloud

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