27 citations · 40 across the 7 of their papers we have counts for
7 papers
OBSR: Open Benchmark for Spatial Representations
Julia Moska, Oleksii Furman, Kacper Kozaczko +4
GeoAI is evolving rapidly, fueled by diverse geospatial datasets like traffic patterns, environmental data, and crowdsourced OpenStreetMap (OSM) information. While sophisticated AI…
A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis
Bruno Martins, Piotr Szymański, Piotr Gramacki
The emergence of Large Language Models (LLMs) has transformed information access, with current LLMs also powering deep research systems that can generate comprehensive report-style…
Evaluation of Code LLMs on Geospatial Code Generation
Piotr Gramacki, Bruno Martins, Piotr Szymański
Software development support tools have been studied for a long time, with recent approaches using Large Language Models (LLMs) for code generation. These models can generate Pytho…
SRAI: Towards Standardization of Geospatial AI
Piotr Gramacki, Kacper Leśniara, Kamil Raczycki +3
Spatial Representations for Artificial Intelligence (srai) is a Python library for working with geospatial data. The library can download geospatial data, split a given area into m…
Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark
Łukasz Augustyniak, Szymon Woźniak, Marcin Gruza +4
Despite impressive advancements in multilingual corpora collection and model training, developing large-scale deployments of multilingual models still presents a significant challe…
Assessment of Massively Multilingual Sentiment Classifiers
Krzysztof Rajda, Łukasz Augustyniak, Piotr Gramacki +3
Models are increasing in size and complexity in the hunt for SOTA. But what if those 2\% increase in performance does not make a difference in a production use case? Maybe benefits…
gtfs2vec -- Learning GTFS Embeddings for comparing Public Transport Offer in Microregions
Piotr Gramacki, Szymon Woźniak, Piotr Szymański
We selected 48 European cities and gathered their public transport timetables in the GTFS format. We utilized Uber's H3 spatial index to divide each city into hexagonal micro-regio…