Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Precise mapping of rapeseed fields at the pre-flowering stage using PlanetScope satellite imagery and convolutional neural networks (related data)

Domaine:

agriculturegeospatial

Type de record:

dataset
Créateur:
YaoJinLi,Li,
Éditeur:
Zenodo
Hôte:avatar
Rapeseed field map of the Hulunbuir region, with data from late June 2023 (pre-flowering stage) and mid-July 2023 (peak flowering stage). The spatial resolution is 3 meters. The coordinate system and projection used is WGS_1984_UTM_Zone_51N. The data is in 1-bit unsigned TIFF format, where a value of 1 represents rapeseed fields and 0 represents non-rapeseed fields. Other items are auxiliary or analysis data.

Visit

doi.orgzenodo.org

Tasks

computer visionimage classification

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Detection of Roof Type in Rural Tanzania using High-Resolution Satellite Imagery and Convolutional Neural NetworksEvaluation of Advanced LULC Classification of Satellite Imagery with Convolutional Neural NetworksUNSUPERVISED SEGMENTATION OF SMALLHOLDER FIELDS IN MOZAMBIQUE USING PLANETSCOPE IMAGERYRwanda 3m Satellite Imagery (PlanetScope)Enhancing Satellite Imagery Resolution for Coastal and Ocean Engineering Applications Using Sub-pixel Convolutional Neural Networks and PixelShuffle TechniquesMultispecies detection and identification of African mammals in aerial imagery using convolutional neural networks

Detection of Roof Type in Rural Tanzania using High-Resolution Satellite Imagery and Convolutional Neural Networks

The United Nations (UN) efforts to completely eliminate poverty by 2030 have been less successful in

Evaluation of Advanced LULC Classification of Satellite Imagery with Convolutional Neural Networks

Accurate Land Use/Land Cover (LULC) classification plays a crucial role in sustainable environmental

UNSUPERVISED SEGMENTATION OF SMALLHOLDER FIELDS IN MOZAMBIQUE USING PLANETSCOPE IMAGERY

Abstract. Smallholders produce about a third of the global crop production. Supporting these smallho

Rwanda 3m Satellite Imagery (PlanetScope)

Detailed 3-Band Satellite Images Capturing Rwanda's Landscapes

Enhancing Satellite Imagery Resolution for Coastal and Ocean Engineering Applications Using Sub-pixel Convolutional Neural Networks and PixelShuffle Techniques

Deep neural networks (DNNs) for super-resolution (SR) address the limitations of low-resolution sate

Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks

Abstract Survey and monitoring of wildlife populations are among the key elements in nature conserv