scholarly journals Towards a typology of land-cover/land-use evolutions using high resolution satellite image time series: application to the metropolitan area of Strasbourg (France)

10.3990/2.391 ◽  
2016 ◽  
Author(s):  
F.N. Guttler ◽  
A. Puissant ◽  
P. Gancarski
Keyword(s):  
Land Use ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Metropolitan Area ◽  
Satellite Image ◽  
High Resolution Satellite Image

Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas

2016 ◽  
Vol 187 ◽  
pp. 156-168 ◽  
Author(s):  
Charlotte Pelletier ◽  
Silvia Valero ◽  
Jordi Inglada ◽  
Nicolas Champion ◽  
Gérard Dedieu
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Random Forests ◽  
Satellite Image ◽  
High Resolution Satellite Image

scholarly journals Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

2017 ◽  
Vol 9 (1) ◽  
pp. 95 ◽  
Author(s):  
Jordi Inglada ◽  
Arthur Vincent ◽  
Marcela Arias ◽  
Benjamin Tardy ◽  
David Morin ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Image ◽  
Land Cover Map

RapidAI4EO: Advancing the State-of-the-Art in Continuous Land Monitoring

2021 ◽  
Author(s):  
Giovanni Marchisio ◽  
Patrick Helber ◽  
Benjamin Bischke ◽  
Tim Davis ◽  
Annett Wania
Keyword(s):  
Land Use ◽  
European Union ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Training Data ◽  
Third Party ◽  
The European Union ◽  
Horizon 2020 ◽  
High Cadence

<p>New catalogues of nearly daily or even intraday temporal data will soon dominate the global archives. However, there has been little exploration of artificial intelligence (AI) techniques to leverage the high cadence that is already possible to achieve through the fusion of multiscale, multimodal sensors. Under the sponsorship of the European Union’s Horizon 2020 programme, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. Focus is on the CORINE Land Cover programme, which is the flagship of CLMS. </p><p>Specific objectives of the project are to: 1) explore and stimulate the development of new spatiotemporal monitoring applications based on the latest advances in AI and Deep Learning (DL); 2) demonstrate the fusion of Copernicus high resolution satellite imagery and third party very high resolution imagery; 3) provide intensified monitoring of Land Use and Land Cover, and Land Use change at a much higher level of detail and temporal cadence than it is possible today. </p><p>Our strategy is two-fold. The first aspect involves developing vastly improved DL architectures to model the phenomenology inherent in high cadence observations with focus on disentangling phenology from structural change. The second involves providing critical training data to drive advancement in the Copernicus community and ecosystem well beyond the lifetime of this project. To this end we will create the most complete and dense spatiotemporal training sets ever, combining Sentinel-2 with daily, harmonized, cloud-free, gap filled, multispectral 3m time series resulting from fusion of open satellite data with Planet imagery at as many as 500,000 patch locations over Europe. The daily time series will span the entire year 2018, to coincide with the latest release of CORINE. We plan to open source these datasets for the benefit of the entire remote sensing community.</p><p>This talk focuses on the description of the datasets whose inspirations comes from the recently released EuroSAT (Helbert et al, 2019) and BigEarthNet corpora (Sumbul et al, 2019). The new corpora will look at the intersection of CORINE 2018 with all the countries in the EU, balancing relative country surface with relative LULC distribution and most notably adding the daily high resolution time series at all locations for the year 2018. Annotations will be based on the CORINE ontology. The higher spatial resolution will support modeling of more LC classes, while the added  temporal dimension should enable disambiguation of land covers across diverse climate zones, as well as an improved understanding of land use.</p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.</p>

scholarly journals Development of segmentation algorithm based region growing for software GIS GRASS

2010 ◽  
Vol 58 (1) ◽  
pp. 207-216
Author(s):  
Aleš Tippner
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Land Cover ◽  
Mathematical Morphology ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Aerial Photographs ◽  
High Resolution Satellite Image

Image segmentation is fundamental prerequisite for new satellite images interpretation methods. GIS GRASS provides segmentation tools enabling global image segmentation only. We designed procedure enabling local segmentation using existing GRASS tools and segmentation algorithm based on region growing that we developed with C++. This algorithm applies mathematical morphology operators to output segments, too. Principial aim of the project is creation of useful input for differentiation of base land cover classes in panchromatic high-resolution satellite image (or historical aerial photographs for example).

Sign in / Sign up

Export Citation Format

Share Document