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Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) architectures, have obtained successful outcomes in timeseries analysis tasks.
BackgroundStudies which have been conducted so far have focused on the processes of land use/cover changes in different areas at regional and global scale.
Accurate and up-to-date information on land use and land cover (LULC) is needed to develop policies on reducing soil sealing through increased urbanization as well as to meet climate targets. More detailed information about building function is also required but is currently lacking.
This manual presents a methodology for assessing woodfuel supply and demand at the level of the displacement camp through the collection of primary data in the field and remote sensing analysis.
The robustness of the physically-based, semi-distributed hydrological model ECOMAG with respect to changing (climatic or land-use) conditions was evaluated for two basins, considered within the modelling workshop held in the frame of the 2013 IAHS conference in Göteborg, Sweden.
Recent progress in very high spatial resolution imagery (VHSRI) has increased the availability of fine‐scale land cover data over extensive areas. This new spatial information might improve our understanding of how land cover affects stream ecosystems.
Over the last 30 years, ecological networks have been deployed to reduce global biodiversity loss by enhancing landscape connectivity.
This article addresses the critical need for a better quantitative understanding of how water resources from the Hérault River catchment in France have been influenced by climate variability and the increasing pressure of human activity over the last 50 years.
The trace metal (TM) content in arable soils has been monitored across a region of France characterised by a large proportion of calcareous soils.
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