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001017987 037__ $$aFZJ-2023-04457
001017987 1001_ $$0P:(DE-HGF)0$$aParis, Claudia$$b0
001017987 1112_ $$aImage and Signal Processing for Remote Sensing XXIX$$cAmsterdam$$d2023-09-03 - 2023-09-06$$wNetherlands
001017987 245__ $$aAccuracy assessment of land-use-land-cover maps: the semantic gap between in situ and satellite data
001017987 260__ $$bSPIE$$c2023
001017987 300__ $$a14
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001017987 520__ $$aThe availability of high-resolution, open, and free satellite data has facilitated the production of global Land-Use-Land-Cover (LULC) maps, which are extremely important to monitor the Earth’s surface constantly. However, generating these maps demands significant efforts in collecting a vast amount of data to train the classifier and to assess their accuracy. Although in-situ surveys are generally regarded as reliable sources of information, it is important to note that there may be inconsistencies between the in-situ data and the information derived from satellite data. This can be attributed to various factors (1) differences in viewpoint perspectives, i.e., aerial versus ground views, and (2) spatial resolution of the satellite images versus the extent of the Land-Cover (LC) present in the scene. The aim of this paper is to explore the feasibility of using geo-referenced street-level imagery to bridge the gap between information provided by field surveys and satellite data. Unlike conventional in-situ surveys that typically provide geo-tagged location-specific information on LULC, street-level images offer a richer semantic context for the sampling point under examination. This allows for (1) an improved interpretation of LC characteristics, and (2) a stronger correlation with satellite data. The experimental analysis was conducted considering the 2018 Land Use and Coverage Area Frame Survey (LUCAS) in-situ data, the LUCAS landscape (street-level) images and three high-resolution thematic products derived from satellite data, namely, Google’s Dynamic World, ESA’s World Cover, and Esri’s Land Cover maps.
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001017987 536__ $$0G:(EU-Grant)956748$$aADMIRE - Adaptive multi-tier intelligent data manager for Exascale (956748)$$c956748$$fH2020-JTI-EuroHPC-2019-1$$x1
001017987 588__ $$aDataset connected to CrossRef Conference
001017987 7001_ $$0P:(DE-HGF)0$$aMartinez-Sanchez, Laura$$b1
001017987 7001_ $$0P:(DE-HGF)0$$avan der Velde, Marijn$$b2
001017987 7001_ $$0P:(DE-Juel1)187558$$aSharma, Surbhi$$b3$$ufzj
001017987 7001_ $$0P:(DE-Juel1)178695$$aSedona, Rocco$$b4$$ufzj
001017987 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b5$$ufzj
001017987 773__ $$a10.1117/12.2679433
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001017987 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178695$$aForschungszentrum Jülich$$b4$$kFZJ
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001017987 9141_ $$y2023
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