Name
Abstract | ||
|---|---|---|
As more and more geospatial data are produced, Big Earth data is becoming a new key to the understanding of the Earth. Such opportunity also comes with new issues and challenges related to the massive and heteregenous amount of data to process and to analyse. The present work explores the use of three types of Earth Observation (EO) data in order to automatically classify built and non-built areas in Africa using a machine learning classifier: SAR (Sentinel) and optical (Landsat) imagery, and the OpenStreetMap (OSM) database as training data. Experimental results in ten african cities show that the use of satellite data from multiple sensors improves the performance of the classifiers in these areas. They also show that using crowd-sourced geospatial databases such as OSM as training data leads to similar accuracies than when relying on field surveys or hand-digitalized datasets. |
Year | Venue | Field |
|---|---|---|
2018 | IGARSS | Geospatial analysis,Training set,Data mining,Computer vision,Computer science,Earth observation,Artificial intelligence,Fusion scheme,Optical imaging,Multiple sensors,Satellite data,Learning classifier system |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yann Forget | 1 | 0 | 0.34 |
| Catherine Linard | 2 | 9 | 4.54 |
| M. Gilbert | 3 | 3 | 1.83 |
| Michal Shimoni | 4 | 56 | 11.17 |
| Juanfran Lopez | 5 | 0 | 0.34 |