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- tutorialAugust 2024
Urban Foundation Models: A Survey
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 6633–6643https://doi.org/10.1145/3637528.3671453Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments. The recent emergence of foundation models such as ...
- research-articleJuly 2024
On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)
- Gengchen Mai,
- Weiming Huang,
- Jin Sun,
- Suhang Song,
- Deepak Mishra,
- Ninghao Liu,
- Song Gao,
- Tianming Liu,
- Gao Cong,
- Yingjie Hu,
- Chris Cundy,
- Ziyuan Li,
- Rui Zhu,
- Ni Lao
ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 10, Issue 2Article No.: 11, Pages 1–46https://doi.org/10.1145/3653070Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes ...
- short-paperDecember 2023
Bavaria Buildings - A Novel Dataset for Building Footprint Extraction, Instance Segmentation, and Data Quality Estimation
SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information SystemsArticle No.: 108, Pages 1–4https://doi.org/10.1145/3589132.3625658Bavaria Buildings is a large, analysis-ready dataset providing openly available co-registered 40cm aerial imagery of Upper Bavaria paired with building footprint information. The Bavaria Buildings dataset (BBD) contains 18205 orthophotos of 2500 × 2500 ...
- short-paperNovember 2022
Towards a foundation model for geospatial artificial intelligence (vision paper)
SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information SystemsArticle No.: 106, Pages 1–4https://doi.org/10.1145/3557915.3561043Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine tuning, few-shot, or even zero-shot learning. Despite their ...
- research-articleOctober 2021
Text Detection and Recognition by using CNNs in the Austro-Hungarian Historical Military Mapping Survey
HIP '21: Proceedings of the 6th International Workshop on Historical Document Imaging and ProcessingPages 25–30https://doi.org/10.1145/3476887.3476904Historical maps include precious data about historical, geographical and economic perspectives of a period. However, several unique challenges and opportunities accompany historical maps compared to modern maps, such as low-quality images, degraded ...