from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce...
This paper aims to develop a supervised classification integrating synthetic aperture radar (SAR) Sentinel-1 (S1) and optical Sentinel-2 (S2) data for land use/land cover (LULC) mapping in a heterogeneous Mediterranean forest area. The time-series of each SAR and optical bands, three optical ...
The data captured in these observations enhances understanding of climate change and land use and improves emergency management response. Land use/land cover (LULC) maps are an increasingly important tool for decision-makers at local, regional, and national government levels around the world. These m...
Image data fusion techniques play a crucial role in combining information from different sensors and platforms to enhance the quality, resolution, and interpretability28. There are different types of data fusion technology including traditional methods, deep learning-based methods, and generative adversarial...
1b. Forested and urban areas are masked using Corine LULC classes (2018). 3. Results In this section, γ0 temporal profiles and regression analysis are shown. As the single-pixel amplitude values can oscillate due to the affection of residual noise, this analysis refers to the mean γ0 ...
and Esri collaboratively. The data collection is derived from ESA Sentinel-2 imagery at 10m resolution globaly using Impact Ovservatory's state of the art deep learning AI land classification model which is trained by billions of human-labeled image pixels. There are 9 LULC classes generated by...
Sentinel-2 and Landsat provide observations of similar nature and offer the opportunity to combine both data sources to increase time-series temporal frequency at high spatial resolution. Multi-sensor image compositing is one way for performing pixel-level data integration and has many advantages for ...
The global LULC time series is available online to more than 10 million users of geographic information system (GIS) software through Esri'sArcGIS Living Atlas of the World, the foremost collection of geographic information and services, including maps and apps. It can also be viewed on the Se...
The results show that an object based classification using only the Sentinel-2 and Landsat 8 image information, without band indexes or ancillary data, offers very similar results for most LULC classes, being the overall accuracy around 87-88% with slightly better results when using Sentinel-2. ...
Deep learning models to map an agricultural expansion area with MODIS and Sentinel-2 time series images Mapping changing land use and land cover (LULC) is important for land management and environment analysis. We tried to build deep learning models to classi... D Luo,M Caldas,H Yang - 《...