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A comprehensive review of the recent advancement in integrating deep learning with geographic information systems
A Raihan - Research Briefs on Information and Communication …, 2023 - rebicte.org
The integration of deep learning (DL) techniques with geographical information system (GIS)
offers a promising avenue for gaining novel insights into environmental phenomena by …
offers a promising avenue for gaining novel insights into environmental phenomena by …
Remote sensing of terrestrial gross primary productivity: a review of advances in theoretical foundation, key parameters and methods
W Zhu, Z ** machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices
Timely and accurate crop yield estimation is important for adjusting agronomic management
and enseuring agricultural sustainability. Machine learning (ML) algorithms provide new …
and enseuring agricultural sustainability. Machine learning (ML) algorithms provide new …
[HTML][HTML] Assessment of the spatiotemporal prediction capabilities of machine learning algorithms on Sea Surface Temperature data: A comprehensive study
S Kartal - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Spatiotemporal time series prediction plays a crucial role in a wide range of applications.
However, in most of the studies, spatial information was ignored and predictions were …
However, in most of the studies, spatial information was ignored and predictions were …
[HTML][HTML] Fostering deep learning approaches to evaluate the impact of urbanization on vegetation and future prospects
Vegetation is an essential component of our global ecosystem and an important indicator of
the dynamics and productivity of land cover. Vegetation forecasting research has been …
the dynamics and productivity of land cover. Vegetation forecasting research has been …
Veg-W2TCN: A parallel hybrid forecasting framework for non-stationary time series using wavelet and temporal convolution network model
Long-term vegetation time series (TS) forecasting based on climatic data is one of the most
challenging topics, capable of assisting in advanced estimation and management for …
challenging topics, capable of assisting in advanced estimation and management for …
[HTML][HTML] Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models
The rising global demand for oil palm emphasizes the importance of accurate oil palm yield
predictions. This predictive capability is critical for effective crop management, supply chain …
predictions. This predictive capability is critical for effective crop management, supply chain …
Multi-modal learning for geospatial vegetation forecasting
Precise geospatial vegetation forecasting holds potential across diverse sectors including
agriculture forestry humanitarian aid and carbon accounting. To leverage the vast …
agriculture forestry humanitarian aid and carbon accounting. To leverage the vast …
Monthly NDVI prediction using spatial autocorrelation and nonlocal attention networks
Accurate prediction of vegetation indices is useful for hel** maintain vegetation stability,
sustaining food production, and reducing socioeconomic losses. The traditional …
sustaining food production, and reducing socioeconomic losses. The traditional …