Cláudia M. Viana
Cláudia M. Viana is a Geospatial Data Scientist and Geographer, holding a PhD in Geography (2022) from the University of Lisbon (UL) with a grant from the Portuguese Foundation for Science and Technology (FCT). She earned a Master's in Geographic Information Systems (2014) and a Degree in Geography (2012), both from the Institute of Geography and Territorial Planning (IGOT-UL). Currently, she is a Post-Doc Junior Researcher at the Centre of Geographical Studies (CEG) at UL, and a member of various research groups, including Modelling and Spatial Planning (MOPT, CEG-ULisboa), Associated Laboratory TERRA, and CEIS20 (UC). Cláudia serves as the Principal Investigator of the AgroecoDecipher project (2022.09372.PTDC) supported by FCT.
Sessions
Most historical sources, available in multiple formats (e.g., tabular and analog data), contain valuable geographic information. This data can be transformed to generate both quantitative and qualitative insights, enabling the creation of digital maps and unlocking significant potential for scientific analysis. However, the use of historical data presents several challenges: 1. Sources need to be digitized; 2. Collections are often spread across multiple archives; 3. Metadata is often unavailable; 4. Standardizing diverse sources and quantitatively reconstructing data from various periods is difficult; 5. The reliability of historical data can be uncertain; 6. There is limited spatial resolution; and 7. Inaccuracies and text legibility issues are common. These challenges underscore the need for novel methodologies aimed at enhancing the quality and quantity of such sources. This paper presents the findings of the exploratory project AgroecoDecipher (2022.09372.PTDC) dedicated to extracting a comprehensive database from historical textual records and analogue map files to trace agroecological patterns. Employing an exploratory methodology grounded in artificial intelligence (AI) and Geographic Information Systems (GIS), the projected solutions include the establish-ment of routines based on AI tools that combines GIS, machine learning (ML), and Large Language Models (LLMs). Approxi-mately 271 survey books from the 1950s were digitized at the municipal level, with a total sheet count exceeding 42,000. Addi-tionally, more than 100 analogue maps were digitized, processed, and vectorized, resulting in a detailed geodatabase map ar-chive. The results are promising and demonstrate that the integration of AI and geospatial tools has proven essential in trans-forming raw historical data.