Zobrazeno 1 - 10
of 2 954
pro vyhledávání: '"LI, Wenwen"'
Autor:
Szwarcman, Daniela, Roy, Sujit, Fraccaro, Paolo, Gíslason, Þorsteinn Elí, Blumenstiel, Benedikt, Ghosal, Rinki, de Oliveira, Pedro Henrique, Almeida, Joao Lucas de Sousa, Sedona, Rocco, Kang, Yanghui, Chakraborty, Srija, Wang, Sizhe, Kumar, Ankur, Truong, Myscon, Godwin, Denys, Lee, Hyunho, Hsu, Chia-Yu, Asanjan, Ata Akbari, Mujeci, Besart, Keenan, Trevor, Arevalo, Paulo, Li, Wenwen, Alemohammad, Hamed, Olofsson, Pontus, Hain, Christopher, Kennedy, Robert, Zadrozny, Bianca, Cavallaro, Gabriele, Watson, Campbell, Maskey, Manil, Ramachandran, Rahul, Moreno, Juan Bernabe
This technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA's Harmonized Landsat and Sentinel-2 data
Externí odkaz:
http://arxiv.org/abs/2412.02732
Autor:
Wang, Sizhe, Li, Wenwen
This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models. Inspired by the concept of location encoding, which aims to capture location characteristics to enha
Externí odkaz:
http://arxiv.org/abs/2411.14560
Autor:
Tian, Yuanyuan, Li, Wenwen, Hu, Lei, Chen, Xiao, Brook, Michael, Brubaker, Michael, Zhang, Fan, Liljedahl, Anna K.
Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval-reranking framework leveraging Large Language Models (LLMs) to enhance the spatiotemporal and semantic associated mining and recommen
Externí odkaz:
http://arxiv.org/abs/2411.12880
Autor:
Wei, Xujun, Zhang, Feng, Zhang, Renhe, Li, Wenwen, Liu, Cuiping, Guo, Bin, Li, Jingwei, Fu, Haoyang, Tang, Xu
In the past few years, Artificial Intelligence (AI)-based weather forecasting methods have widely demonstrated strong competitiveness among the weather forecasting systems. However, these methods are insufficient for high-spatial-resolution short-ter
Externí odkaz:
http://arxiv.org/abs/2411.10144
Geospatial Knowledge Graphs (GeoKGs) model geoentities (e.g., places and natural features) and spatial relationships in an interconnected manner, providing strong knowledge support for geographic applications, including data retrieval, question-answe
Externí odkaz:
http://arxiv.org/abs/2410.18345
Autor:
Shimizu, Cogan, Stephe, Shirly, Barua, Adrita, Cai, Ling, Christou, Antrea, Currier, Kitty, Dalal, Abhilekha, Fisher, Colby K., Hitzler, Pascal, Janowicz, Krzysztof, Li, Wenwen, Liu, Zilong, Mahdavinejad, Mohammad Saeid, Mai, Gengchen, Rehberger, Dean, Schildhauer, Mark, Shi, Meilin, Norouzi, Sanaz Saki, Tian, Yuanyuan, Wang, Sizhe, Wang, Zhangyu, Zalewski, Joseph, Zhou, Lu, Zhu, Rui
KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, cr
Externí odkaz:
http://arxiv.org/abs/2410.13948
Research on geospatial foundation models (GFMs) has become a trending topic in geospatial artificial intelligence (AI) research due to their potential for achieving high generalizability and domain adaptability, reducing model training costs for indi
Externí odkaz:
http://arxiv.org/abs/2409.00489
Autor:
Fajstrup, Lisbeth, Fasy, Brittany Terese, Li, Wenwen, Mezrag, Lydia, Rask, Tatum, Tombari, Francesca, Urbančič, Živa
The Gromov--Hausdorff distance measures the similarity between two metric spaces by isometrically embedding them into an ambient metric space. In this work, we introduce an analogue of this distance for metric spaces endowed with directed structures.
Externí odkaz:
http://arxiv.org/abs/2408.14394
Understanding the complex nature of spatial information is crucial for problem solving in social and environmental sciences. This study investigates how the underlying patterns of spatial data can significantly influence the outcomes of spatial predi
Externí odkaz:
http://arxiv.org/abs/2408.14722
Autor:
Jin, Benjamin, Hernández, Maria del C. Valdés, Fontanella, Alessandro, Li, Wenwen, Platt, Eleanor, Armitage, Paul, Storkey, Amos, Wardlaw, Joanna M., Mair, Grant
As a potential non-invasive biomarker for ischaemic stroke, intracranial arterial calcification (IAC) could be used for stroke risk assessment on CT head scans routinely acquired for other reasons (e.g. trauma, confusion). Artificial intelligence met
Externí odkaz:
http://arxiv.org/abs/2408.01199