Accuracy assessment of land-use-land-cover maps: the semantic gap between in situ and satellite data

Claudia Paris; Laura Martinez-Sanchez; Marijn van der Velde; Surbhi Sharma; Rocco Sedona; Gabriele Cavallaro

doi:10.1117/12.2679433

19 October 2023 Accuracy assessment of land-use-land-cover maps: the semantic gap between in situ and satellite data

Claudia Paris, Laura Martinez-Sanchez, Marijn van der Velde, Surbhi Sharma, Rocco Sedona, Gabriele Cavallaro

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 127330M (2023) https://doi.org/10.1117/12.2679433
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

The 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.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Claudia Paris, Laura Martinez-Sanchez, Marijn van der Velde, Surbhi Sharma, Rocco Sedona, and Gabriele Cavallaro "Accuracy assessment of land-use-land-cover maps: the semantic gap between in situ and satellite data", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 127330M (19 October 2023); https://doi.org/10.1117/12.2679433

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