Publications

@article{rs14122812,

title = {Encoding Geospatial Vector Data for Deep Learning: LULC as a Use Case},

author = {Marvin Mc Cutchan and Ioannis Giannopoulos},

url = {https://www.mdpi.com/2072-4292/14/12/2812},

doi = {10.3390/rs14122812},

issn = {2072-4292},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Remote Sensing},

volume = {14},

number = {12},

abstract = {Geospatial vector data with semantic annotations are a promising but complex data source for spatial prediction tasks such as land use and land cover (LULC) classification. These data describe the geometries and the types (i.e., semantics) of geo-objects, such as a Shop or an Amenity. Unlike raster data, which are commonly used for such prediction tasks, geospatial vector data are irregular and heterogenous, making it challenging for deep neural networks to learn based on them. This work tackles this problem by introducing novel encodings which quantify the geospatial vector data allowing deep neural networks to learn based on them, and to spatially predict. These encodings were evaluated in this work based on a specific use case, namely LULC classification. We therefore classified LULC based on the different encodings as input and an attention-based deep neural network (called Perceiver). Based on the accuracy assessments, the potential of these encodings is compared. Furthermore, the influence of the object semantics on the classification performance is analyzed. This is performed by pruning the ontology, describing the semantics and repeating the LULC classification. The results of this work suggest that the encoding of the geography and the semantic granularity of geospatial vector data influences the classification performance overall and on a LULC class level. Nevertheless, the proposed encodings are not restricted to LULC classification but can be applied to other spatial prediction tasks too. In general, this work highlights that geospatial vector data with semantic annotations is a rich data source unlocking new potential for spatial predictions. However, we also show that this potential depends on how much is known about the semantics, and how the geography is presented to the deep neural network.},

keywords = {AI, Deep learning, geoinformation, geosemantics, LULC, Volunteered Geographic Information},

pubstate = {published},

tppubtype = {article}

}

@article{giannopoulos19[TUW-286360],

title = {Geheimnisse, die von Wänden undErdoberflächen versteckt werden, einfach ansehen?},

author = {Ioannis Giannopoulos},

url = {https://publik.tuwien.ac.at/files/publik_286360.pdf},

year  = {2019},

date = {2019-01-01},

journal = {Bulletin TU Wien alumni club},

volume = {47},

number = {Juni},

abstract = {Während wir uns im Außenbereich bewegen und mit dem Raum interagieren, nehmen wir nur physische Objekte wie Gebäude, Straßen und andere Fußgänger in der Nähe wahr. Aber unsere Welt ist voll verborgener Informationen, z.B. unterirdischen Elemente, die von der Bodenoberfläche und den Gebäudewänden verdeckt werden oder von georeferenzierten Informationen, die im Internet, aber nicht im realen Raum zu finden sind. Diese Informationen können Geschichten erzählen oder sogar einen bestimmten Ort charakterisieren},

keywords = {augmented reality, geoinformation, unterirdisch},

pubstate = {published},

tppubtype = {article}

}