TY - GEN

T1 - Improved classification of satellite imagery using spatial feature maps extracted from social media

AU - Leichter, Artem

AU - Wittich, Dennis

AU - Rottensteiner, Franz

AU - Werner, Martin

AU - Sester, Monika

N1 - Funding information: This work was partially funded by the Federal Ministry of Education and Research, Germany (Bundesministerium für Bildung und Forschung, Förderkennzeichen 01IS17076). We gratefully acknowledge this support.

PY - 2018

Y1 - 2018

N2 - In this work, we consider the exploitation of social media data in the context of Remote Sensing and Spatial Information Sciences. To this end, we explore a way of augmenting and integrating information represented by geo-located feature vectors into a system for the classification of satellite images. For that purpose, we present a quite general data fusion framework based on Convolutional Neural Network (CNN) and an initial examination of our approach on features from geo-located social media postings on the Twitter and Sentinel images. For this examination, we selected six simple Twitter features derived from the metadata, which we believe could contain information for the spatial context. We present initial experiments using geotagged Twitter data from Washington DC and Sentinel images showing this area. The goal of classification is to determine local climate zones (LCZ). First, we test whether our selected feature maps are not correlated with the LCZ classification at the geo-tag position. We apply a simple boost tree classifier on this data. The result turns out not to be a mere random classifier. Therefore, this data can be correlated with LCZ. To show the improvement by our method, we compare classification with and without the Twitter feature maps. In our experiments, we apply a standard pixel-based CNN classification of the Sentinel data and use it as a baseline model. After that, we expand the input augmenting additional Twitter feature maps within the CNN and assess the contribution of these additional features to the overall F1-score of the classification, which we determine from spatial cross-validation.

AB - In this work, we consider the exploitation of social media data in the context of Remote Sensing and Spatial Information Sciences. To this end, we explore a way of augmenting and integrating information represented by geo-located feature vectors into a system for the classification of satellite images. For that purpose, we present a quite general data fusion framework based on Convolutional Neural Network (CNN) and an initial examination of our approach on features from geo-located social media postings on the Twitter and Sentinel images. For this examination, we selected six simple Twitter features derived from the metadata, which we believe could contain information for the spatial context. We present initial experiments using geotagged Twitter data from Washington DC and Sentinel images showing this area. The goal of classification is to determine local climate zones (LCZ). First, we test whether our selected feature maps are not correlated with the LCZ classification at the geo-tag position. We apply a simple boost tree classifier on this data. The result turns out not to be a mere random classifier. Therefore, this data can be correlated with LCZ. To show the improvement by our method, we compare classification with and without the Twitter feature maps. In our experiments, we apply a standard pixel-based CNN classification of the Sentinel data and use it as a baseline model. After that, we expand the input augmenting additional Twitter feature maps within the CNN and assess the contribution of these additional features to the overall F1-score of the classification, which we determine from spatial cross-validation.

KW - Classification

KW - Data fusion

KW - Deep learning

KW - Satellite images

KW - Social media mining

UR - http://www.scopus.com/inward/record.url?scp=85056156143&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-XLII-4-335-2018

DO - 10.5194/isprs-archives-XLII-4-335-2018

M3 - Conference contribution

AN - SCOPUS:85056156143

T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

SP - 403

EP - 410

BT - Proceedings of Mid-term Symposium “3D Spatial Information Science – The Engine of Change”

T2 - ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change

Y2 - 1 October 2018 through 5 October 2018

ER -