Iterative integration of deep learning in hybrid Earth surface system modelling
Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …
processes. Deep learning (DL), with the data-driven strength of neural networks, has …
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
Abstract Google Earth Engine (GEE) is a cloud-based geospatial processing platform for
large-scale environmental monitoring and analysis. The free-to-use GEE platform provides …
large-scale environmental monitoring and analysis. The free-to-use GEE platform provides …
Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …
management, environmental modelling and assessment, and agricultural production …
Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
[HTML][HTML] Nighttime light remote sensing for urban applications: Progress, challenges, and prospects
Nighttime light (NTL) remote sensing data offer unique capabilities to characterize both the
extent and intensity of human activities and have been extensively used to understand …
extent and intensity of human activities and have been extensively used to understand …
Smart agriculture–Urgent need of the day in develo** countries
Smart agriculture is based primarily on three platforms, namely science, innovation and
space technologies. These are considered as the three pioneer pillars of nation building …
space technologies. These are considered as the three pioneer pillars of nation building …
Machine learning classification of mediterranean forest habitats in google earth engine based on seasonal sentinel-2 time-series and input image composition …
The sustainable management of natural heritage is presently considered a global strategic
issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) …
issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) …
Spatiotemporal change analysis and prediction of future land use and land cover changes using QGIS MOLUSCE plugin and remote sensing big data: a case study of …
R Muhammad, W Zhang, Z Abbas, F Guo… - Land, 2022 - mdpi.com
Land use and land cover (LULC) change analysis is a systematic technique that aids in the
comprehension of physical and non-physical interaction with the natural habitat and the …
comprehension of physical and non-physical interaction with the natural habitat and the …
Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation
Due to its wide applications, remote sensing (RS) image semantic segmentation has
attracted increasing research interest in recent years. Benefiting from its hierarchical abstract …
attracted increasing research interest in recent years. Benefiting from its hierarchical abstract …