# GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Research

arXiv: https://arxiv.org/abs/2210.13207 · HTML: https://mlsanity.com/paper/2210.13207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 76 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges](https://mlsanity.com/paper/1707.02432/) (2017)
- [Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tags](https://mlsanity.com/paper/2111.00970/) (2021)
- [Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection](https://mlsanity.com/paper/2112.06104/) (2021)
- [Trinity: A No-Code AI platform for complex spatial datasets](https://mlsanity.com/paper/2106.11756/) (2021)
- [Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States](https://mlsanity.com/paper/1805.08946/) (2018)
- [City-level Geolocation of Tweets for Real-time Visual Analytics](https://mlsanity.com/paper/1910.02213/) (2019)
- [Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics](https://mlsanity.com/paper/1908.04383/) (2019)

## Abstract (arXiv)

Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption. However, the efficient design and implementation of GeoAI systems face many open challenges. This is mainly due to the lack of non-standardized approaches to artificial intelligence tool development, inadequate platforms, and a lack of multidisciplinary engagements, which all motivate domain experts to seek a shared stage with scientists and engineers to solve problems of significant impact on society. Since its inception in 2017, the GeoAI series of workshops has been co-located with the Association for Computing Machinery International Conference on Advances in Geographic Information Systems. The workshop series has fostered a nexus for geoscientists, computer scientists, engineers, entrepreneurs, and decision-makers, from academia, industry, and government to engage in artificial intelligence, spatiotemporal data computing, and geospatial data science research, motivated by various challenges. In this article, we revisit and discuss the state of GeoAI open research directions, the recent developments, and an emerging agenda calling for a continued cross-disciplinary community engagement.