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 …
Application of deep learning architectures for satellite image time series prediction: A review
Satellite image time series (SITS) is a sequence of satellite images that record a given area
at several consecutive times. The aim of such sequences is to use not only spatial …
at several consecutive times. The aim of such sequences is to use not only spatial …
[LIVRE][B] Spatial data science: With applications in R
Spatial Data Science introduces fundamental aspects of spatial data that every data scientist
should know before they start working with spatial data. These aspects include how …
should know before they start working with spatial data. These aspects include how …
[LIVRE][B] Geocomputation with R
Geocomputation with R is for people who want to analyze, visualize and model geographic
data with open source software. It is based on R, a statistical programming language that …
data with open source software. It is based on R, a statistical programming language that …
Multi-objective land use optimization based on integrated NSGA–II–PLUS model: Comprehensive consideration of economic development and ecosystem services …
C Luan, R Liu, Q Zhang, J Sun, J Liu - Journal of Cleaner Production, 2024 - Elsevier
Urbanization and industrialization have resulted in an excessive intensity of land use,
structural imbalances, and regional ecological deterioration. To achieve sustainable …
structural imbalances, and regional ecological deterioration. To achieve sustainable …
Think global, cube local: an Earth Observation Data Cube's contribution to the Digital Earth vision
The technological landscape for managing big Earth observation (EO) data ranges from
global solutions on large cloud infrastructures with web-based access to self-hosted …
global solutions on large cloud infrastructures with web-based access to self-hosted …
Deforestation detection using a spatio-temporal deep learning approach with synthetic aperture radar and multispectral images
Deforestation is a global change driver that contributes to atmospheric carbon emissions,
causes biodiversity loss and ecosystem services degradation. Usually, this process has …
causes biodiversity loss and ecosystem services degradation. Usually, this process has …
Cognitive soil digital twin for monitoring the soil ecosystem: a conceptual framework
The digital twin concept has found widespread application across diverse industries. Herein,
we present a comprehensive conceptual framework for the cognitive soil digital twin, which …
we present a comprehensive conceptual framework for the cognitive soil digital twin, which …
Drowning in data, thirsty for information and starved for understanding: A biodiversity information hub for cooperative environmental monitoring in South Africa
The world is firmly cemented in a notitian age (Latin: notitia, meaning data)–drowning in
data, yet thirsty for information and the synthesis of knowledge into understanding. As …
data, yet thirsty for information and the synthesis of knowledge into understanding. As …
Comparison of various models for multi-scenario simulation of land use/land cover to predict ecosystem service value: A case study of Harbin-Changchun Urban …
C Luan, R Liu, Y Li, Q Zhang - Journal of Cleaner Production, 2024 - Elsevier
Understanding the intricate changes in land use and land cover (LULC) transformations, as
well as accurately quantifying the ecosystem services value (ESV), holds paramount …
well as accurately quantifying the ecosystem services value (ESV), holds paramount …