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Found 5 papers, 2 papers with code
Benchmarking Deep Learning Architectures for Urban Vegetation Point Cloud Semantic Segmentation from MLS
PointMetaBase and KPConv (omni-supervised) achieve the highest mIoU on the Chandigarh (95. 24%) and Toronto3D datasets (91. 26%), respectively while PointCNN provides the highest mIoU on the Kerala dataset (85. 68%).
Translating Place-Related Questions to GeoSPARQL Queries
Many place-related questions can only be answered by complex spatial reasoning, a task poorly supported by factoid question retrieval.
Templates of generic geographic information for answering where-questions
In this paper, we present templates that allow to characterize the human-generated answers and to imitate their structure.
Disambiguating fine-grained place names from descriptions by clustering
Everyday place descriptions often contain place names of fine-grained features, such as buildings or businesses, that are more difficult to disambiguate than names referring to larger places, for example cities or natural geographic features.
Geo-referencing Place from Everyday Natural Language Descriptions
Current techniques for geo-referencing places from text documents are using place name recognition and disambiguation; however, place descriptions often contain place references that are not known by gazetteers, or that are expressed in other, more flexible ways.
