Aerial and satellite images and labels to train deep learning models to detect urban canopies

DOI

Explore a comprehensive dataset featuring over 14000 labelled urban tree canopies in images from across the globe, specifically curated for advancing tree detection methodologies. The dataset comprises image tiles in both .tif, .jpg and .png formats, following the Pascal VOC and YOLO standards. Additionally, we included a .csv summary file with all annotations. To enhance usability, labels are provided in three formats: .xml, and .txt. The RGB tiles, utilized in this dataset, are openly accessible.

Identifier
DOI https://doi.org/10.34810/data1151
Metadata Access https://dataverse.csuc.cat/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34810/data1151
Provenance
Creator Velasquez-Camacho, Luisa ORCID logo; Fonseca, Bruno Augusto ORCID logo; Etxegarai, Maddi ORCID logo; Miguel Magaña, Sergio de ORCID logo
Publisher CORA.Repositori de Dades de Recerca
Contributor Velasquez-Camacho, Luisa; Eurecat Centre Tecnològic de Catalunya; Universitat de Lleida; Miguel Magaña, Sergio de; Etxegarai, Maddi; Eurecat, Centre Tecnològic
Publication Year 2024
Funding Reference Eurecat Programa de Beques de Doctorat Vicente Lopez
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Velasquez-Camacho, Luisa (Universitat de Lleida)
Representation
Resource Type Observation data/ratings; Dataset
Format text/plain; text/tab-separated-values; application/zip
Size 4920; 318583; 108982413
Version 1.0
Discipline Earth and Environmental Science; Environmental Research; Geosciences; Natural Sciences
Spatial Coverage Lleida, Spain