CropSight: Towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and PlanetScope satellite imagery-Reference-Cited by-同舟云学术

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CropSight: Towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and PlanetScope satellite imagery

Published:2024-10 Issue: Volume:216 Page:66-89
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Liu Yin,Diao Chunyuan,Mei Weiye,Zhang Chishan

Funder

NASA

USDA

NSF

National Science Foundation

Publisher

Elsevier BV

Reference79 articles.

1. BARF: a new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification;Abdar;Inf. Sci.,2021

2. A review of uncertainty quantification in deep learning: techniques, applications and challenges;Abdar;Inf. Fusion,2021

3. Google street view: capturing the world at street level;Anguelov;Computer,2010

4. Uncertainty-driven ensembles of multi-scale deep architectures for image classification;Arco;Inf. Fusion,2023

5. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis;Belgiu;Remote Sens. Environ.,2018

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