Conference Agenda

Dengue, one of the main global threats to public health, has experienced a significant increase in its incidence due to the geographic expansion of the Aedes aegypti and Aedes albopictus vectors. This phenomenon is driven by factors such as climate change, urbanization and the emergence of urban heat islands (UHIs), which lead to elevated temperatures that promote the development and transmission of the virus. This study evaluates the spatial and temporal variations of land surface temperature (LST) in Córdoba, Argentina, using remote sensors such as MODIS and Landsat, with the objective of analyzing their relationship with UHIs and Ae. aegypti activity. Besides, we analyzed LST dynamics across data from 2014 to 2024 in a recently urbanized area in order to assess the thermal impact of land cover change. Spatial analysis revealed up to 5 °C differences in LST within the city, with significantly higher values in areas with reduced vegetation. In a specific case, the conversion of a vegetated area into a public park led to an average LST increase of 4 °C over six years, showing greater thermal intensification than a nearby stable vegetated site. Cluster analysis grouped mosquito trap locations based on LST levels, revealing consistent seasonal trends and lower normalized difference vegetation index (NDVI) values in hotter areas. Although the correlation between LST and Ae. aegypti egg counts was generally weak, it strengthened when time lags were considered, with oviposition peaks occurring approximately four weeks after temperature peaks. Our findings confirm the influence of urban land cover changes on thermal variations and its potential to affect Ae. aegypti dynamics. As urban expansion continues, integrating thermal landscape monitoring into vector surveillance systems may help anticipate shifts in mosquito activity and dengue risk. Additionally, our results highlight the importance of using LST data at higher temporal frequency for enhanced vector risk assessment.

Geocoding is a fundamental process for epidemiology, as it allows for the spatial analysis of disease distribution, such as dengue, with accuracy being a critical factor for study validity. The objective of this study was to test three geocoding methods using a database of probable dengue cases from 2007 to 2024 in the Federal District, Brazil. The techniques evaluated were: the Geocoding API (Google Maps), the Nominatim API (OpenStreetMap - OSM), and a method developed with Artificial Intelligence (AI) that uses a Large Language Model (LLM) with Retrieval Augmented Generation (RAG) based on the CNEFE - IBGE address database. In the results, the Geocoding API (Google Maps) obtained the highest return rate (99.6%), followed by the LLM (AI) (98%) and Nominatim (OSM) with only 11.5%. However, in terms of accuracy, the Google Maps API showed the largest errors and outliers. The OSM technique was the most accurate in the validated sample, but it extrapolated results to other countries in cases of high ambiguity. The LLM (AI) approach proved robust, both due to its high return capability and its precision errors. Furthermore, it offers greater flexibility in identifying and correcting errors in the geocoding process. The OSM and LLM (AI) techniques are viable candidates, and the combination of methods can optimize the final quality in future analyses and ensure the applicability of spatial analyses in public health contexts.

For decades, the focus of geospatial artificial intelligence (AI) has been on imagery and data, with linguistic interfaces remaining unexplored. This study presents a systematic review of research applying Large Language Models (LLMs) to Cartography and GIScience. We analysed 54 peer-reviewed articles published between 2023 and 2025, mapping the use of LLMs in data acquisition, semantic enrichment, spatial analysis, cartographic design, and user interaction, among other topics. The corpus reveals four dominant application clusters: (1) semantic creation/alignment of Geo-knowledge graphs; (2) text- and vision-based data acquisition; (3) language-driven spatial analysis and GeoQA; and (4) automated map styling and symbolisation. GPT-3.5/4 underpins two-thirds of the studies, while open-weight models, such as LLaMA-2, FLAN-T5, Gemini, and DeepSeek, are gaining traction. LLM work aligns most strongly with the challenges of openness and reproducibility, as well as cartographic design automation, but is noticeably weaker in areas such as provenance ethics, causal inference, participatory mapping, and mobile multimodal interaction. We outline three priorities for future research: (i) open benchmark datasets for spatial reasoning and map quality; (ii) ethics checklists that surface bias, privacy and hallucination risks; and (iii) investment in multilingual, low-resource Geo-LLMs to broaden global participation. By mapping current advances against long-standing research gaps, the review provides an actionable agenda for guiding large language models (LLMs) toward equitable and trustworthy cartographic practice.

This paper introduces an ontology-driven model aligned with the Semantic Sensor Network Ontology (SSN) to formally describe spectral observations from remote sensing instruments. The model addresses key limitations in existing frameworks by explicitly representing the internal structure of spectral data—such as the relationship between wavelength intervals and measured intensities—using description logic-based constructs. The proposed ontology integrates principles from the Quantities, Units, Dimensions and Data Types (QUDT) ontology and introduces new classes to describe the basis of spectral measurements, supporting multispectral and hyperspectral data. By enabling machine-readable representations of spectra, this framework enhances semantic interoperability, facilitates advanced queries, and supports use cases such as spectral data fusion, validation, and transformation across sensors. Implementation examples include real-world instruments such as the ASD FieldSpec 4 HiRes spectroradiometer and Landsat 8 OLI sensor, demonstrating the utility of the model. The framework contributes to the development of FAIR-aligned, semantically rich infrastructures for Earth Observation data.

The Master’s in Applications of Spatial Information (MAIE), jointly offered by CONAE and the National University of Córdoba through the Gulich Institute, represents a pioneering initiative in Latin America for the training of professionals in applied Earth observation. Over 16 years and with more than 100 graduates, the program has built a strong interdisciplinary foundation that integrates space technology with problem-solving approaches relevant to the Global South. This study presents a comprehensive analysis of the program’s evolution, including student demographics, funding mechanisms, and thesis topics. It highlights the program’s unique strengths, such as its alignment with the National Space Plan, personalized research mentorship, and partnerships with institutions like the Italian Space Agency. Thesis research spans diverse themes—ranging from environmental and disasters monitoring, epidemiology to food security and geospatial data science—employing varied satellite data sources. The findings underscore MAIE’s critical role in fostering technological sovereignty, sustainable development, and territorial intelligence, while also identifying areas for improvement in terms of equity and financial sustainability. In doing so, the paper positions MAIE as a key knowledge infrastructure for addressing socio-environmental challenges in Latin America and beyond.