Reproducible mapping of marine biodiversity using autonomous surface vehicles and deep learning

Spatiotemporal prediction of grass curing in Victoria, Australia using Multi-Source remote sensing and deep learning models

Liangwei Liao, Xuan Zhu

Hybrid physics–AI aerosol property retrieval algorithm for AMI/GK-2A with a deep learning radiative transfer emulator

Min-Suek Kim, Jhoon Kim, H. Lim, Seoyoung Lee, Hyejin Cha, Yujing Chai, et al.

SAR2LiDAR: A deep learning-based cross-modal matching framework between SAR images and LiDAR point clouds

Yifan Zhang, J. Mallorquí, Wen Wang

Geomorphology-constrained bathymetric inversion of turbid tidal channels using centerline-network priors and machine learning

Jianchun Chen, Chao Gao, Yan Gu, Xiangyu Ou, Xiaoyi Cheng, Shibing Zhu, et al.

A machine learning framework for predicting agricultural terrace suitability using positive and unlabelled datasets: an application for the Troodos Mountains, Cyprus

A. Meena, C. Zoumides, H. Djuma, I. Sofokleous, C. Camera, A. Bruggeman

Enabling maize mapping in double-cropping regions of Argentina and Brazil without ground reference data by leveraging multiple satellite platforms

Ryoungseob Kwon, Youngryel Ryu, Philippe Ciais, David Makowski, Changhyun Choi, Huaize Feng, et al.

Large-scale maize mapping is challenging due to the lack of field-level datasets, which were essential for training machine learning models. The United States (US) provides abundant in-situ labels and national maize maps while Argentina and Brazil offered limited data and double-…Read more