Zobrazeno 1 - 10
of 2 239
pro vyhledávání: '"Ferres A"'
Autor:
Crowe, Samuel, Fedriani, Rubén, Tan, Jonathan C., Kinman, Alva, Zhang, Yichen, Andersen, Morten, Ferres, Lucía Bravo, Nogueras-Lara, Francisco, Schödel, Rainer, Bally, John, Ginsburg, Adam, Cheng, Yu, Yang, Yao-Lun, Kendrew, Sarah, Law, Chi-Yan, Armstrong, Joseph, Li, Zhi-Yun
We present James Webb Space Telescope (JWST)-NIRCam observations of the massive star-forming molecular cloud Sagittarius C (Sgr C) in the Central Molecular Zone (CMZ). In conjunction with ancillary mid-IR and far-IR data, we characterize the two most
Externí odkaz:
http://arxiv.org/abs/2410.09253
Autor:
Naushirvanov, Timur, Elejalde, Erick, Kalimeri, Kyriaki, Omodei, Elisa, Karsai, Márton, Ferres, Leo
Climate change is altering the frequency and intensity of wildfires, leading to increased evacuation events that disrupt human mobility and socioeconomic structures. These disruptions affect access to resources, employment, and housing, amplifying ex
Externí odkaz:
http://arxiv.org/abs/2410.06017
Autor:
Kerner, Hannah, Chaudhari, Snehal, Ghosh, Aninda, Robinson, Caleb, Ahmad, Adeel, Choi, Eddie, Jacobs, Nathan, Holmes, Chris, Mohr, Matthias, Dodhia, Rahul, Ferres, Juan M. Lavista, Marcus, Jennifer
Crop field boundaries are foundational datasets for agricultural monitoring and assessments but are expensive to collect manually. Machine learning (ML) methods for automatically extracting field boundaries from remotely sensed images could help real
Externí odkaz:
http://arxiv.org/abs/2409.16252
Autor:
Casaburi, Pasquale, Dall'Amico, Lorenzo, Gozzi, Nicolò, Kalimeri, Kyriaki, Sapienza, Anna, Schifanella, Rossano, Di Matteo, T., Ferres, Leo, Mazzoli, Mattia
The COVID19 pandemic highlighted the importance of non-traditional data sources, such as mobile phone data, to inform effective public health interventions and monitor adherence to such measures. Previous studies showed how socioeconomic characterist
Externí odkaz:
http://arxiv.org/abs/2405.19141
Autor:
Hernandez, Andres, Miao, Zhongqi, Vargas, Luisa, Dodhia, Rahul, Arbelaez, Pablo, Ferres, Juan M. Lavista
The alarming decline in global biodiversity, driven by various factors, underscores the urgent need for large-scale wildlife monitoring. In response, scientists have turned to automated deep learning methods for data processing in wildlife monitoring
Externí odkaz:
http://arxiv.org/abs/2405.12930
Autor:
Tadesse, Girmaw Abebe, Robinson, Caleb, Hacheme, Gilles Quentin, Zaytar, Akram, Dodhia, Rahul, Shawa, Tsering Wangyal, Ferres, Juan M. Lavista, Kreike, Emmanuel H.
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- Waterholes, Omuti homesteads, and Big trees -- around Oshikango in Namibia u
Externí odkaz:
http://arxiv.org/abs/2404.08544
Autor:
Ahamed, Shadab, Xu, Yixi, Bloise, Ingrid, O, Joo H., Uribe, Carlos F., Dodhia, Rahul, Ferres, Juan L., Rahmim, Arman
Publikováno v:
Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641Q (3 April 2023)
Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a higher probability of containing tumors, thereby directing physicians a
Externí odkaz:
http://arxiv.org/abs/2403.07105
Autor:
Zaytar, Akram, Robinson, Caleb, Hacheme, Gilles Q., Tadesse, Girmaw A., Dodhia, Rahul, Ferres, Juan M. Lavista, Hughey, Lacey F., Stabach, Jared A., Amoke, Irene
Rare object detection is a fundamental task in applied geospatial machine learning, however is often challenging due to large amounts of high-resolution satellite or aerial imagery and few or no labeled positive samples to start with. This paper addr
Externí odkaz:
http://arxiv.org/abs/2403.02736
Autor:
Robinson, Caleb, Corley, Isaac, Ortiz, Anthony, Dodhia, Rahul, Ferres, Juan M. Lavista, Najafirad, Peyman
Fully understanding a complex high-resolution satellite or aerial imagery scene often requires spatial reasoning over a broad relevant context. The human object recognition system is able to understand object in a scene over a long-range relevant con
Externí odkaz:
http://arxiv.org/abs/2401.06762
Autor:
Roman, Anthony Cintron, Vaughan, Jennifer Wortman, See, Valerie, Ballard, Steph, Torres, Jehu, Robinson, Caleb, Ferres, Juan M. Lavista
This paper introduces a no-code, machine-readable documentation framework for open datasets, with a focus on responsible AI (RAI) considerations. The framework aims to improve comprehensibility, and usability of open datasets, facilitating easier dis
Externí odkaz:
http://arxiv.org/abs/2312.06153