Sasan Tavakkol
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Piaget: A Probabilistic Inference Approach for Geolocating Historical Buildings
Cyrus Shahabi
Feng Han
Raimondas Kiveris
IEEE International Conference on Big Data (2021)
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We aim to find the geographical location of (geolocate) a large number of old buildings facades extracted from historical photographs. We can acquire the geo-coordinates of some of these facades either through crowdsourcing or exploring their metadata. Using these “seed” buildings and through spatial reasoning within and across the historical pictures, in this paper, we show how we infer the geolocation of the other facades. We propose a probabilistic inference approach that first constructs a graph with facades as nodes and their spatial distances as edges, and then through probabilistic inference on this graph, geolocate the facades. Our experiments show that with 10\% of the building geolocated as seed buildings, we can quite accurately geolocate the rest of the buildings in our dataset.
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An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images
Zekun Li
Yao-Yi Chiang
Basel Shbita
Johannes H. Uhl
Stefan Leyk
Craig Knoblock
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2020)
Preview abstract
Historical maps contain detailed geographic information difficult to find elsewhere covering long-periods of time (e.g., 125 years for the historical topographic maps in the US). However, these maps typically exist as scanned images without searchable metadata. Existing approaches making historical maps searchable rely on tedious manual work (including crowd-sourcing) to generate the metadata (e.g., geolocations and keywords). Optical character recognition (OCR) software could alleviate the required manual work, but the recognition results are individual words instead of location phrases (e.g., ``Black'' and ``Mountain'' vs. ``Black Mountain''). This paper presents an end-to-end approach to address the real-world problem of finding and indexing historical map images. This approach automatically processes historical map images to extract their text content and generates a set of metadata that is linked to large external geospatial knowledge bases. The linked metadata in the RDF (Resource Description Framework) format support complex queries for finding and indexing historical maps, such as retrieving all historical maps covering mountain peaks higher than 1,000 meters in California. We have implemented the approach in a system called mapKurator. We have evaluated mapKurator using historical maps from several sources with various map styles, scales, and coverage. Our results show significant improvement over the state-of-the-art methods. The code has been made publicly available as modules of the Kartta Labs project at https://github.com/kartta-labs/Project.
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Kartta Labs: Unrendering Historical Maps
Yao-Yi Chiang
Tim Waters
Feng Han
Kisalaya Prasad
Raimondas Kiveris
Proceedings of the 3nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. ACM, 2019. (2019)
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This paper introduces the Kartta Labs, an ongoing open-source and open-data project aiming for organizing the world’s historical maps and making them universally accessible and useful. Kartta Labs’ framework is designed as a composition of multiple modules. Each module includes a crowdsourcing component and an intelligent, machine-assisted component to automate the process. The framework takes images of historical maps, registers them in space and time, generates a vector version of the map content, and allows the users to query for the vector content and recreate the historical maps in various cartographic styles. We refer to this process as unrendering. The resulting machine-readable map data support a variety of scientific studies and applications that require long-term, detailed geographic information in the past and open up opportunities in other areas such as entertainment. The paper also presents the preliminary results from one automated module to geolocalize a guven a historical map.
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