Sometimes we want more than accurately predicting. Our results show EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024 Image Segmentation • Updated 19 minutes ago• 146 ViktorDo published a dataset7 days ago Remote sensing data come in a variety of formats, acquired by sensors operating at different spatial scales and through distinct physical Training is conducted following the Segformer original implementation, using a batch size of 8 for 265 epochs, using the AdamW optimizer with an initial learning rate of 6e-5, weight decay of Courses taught by ECEO Projects currently available Exemples of past projects, completed at ECEO The usage of digital technologies (from camera traps to drones) can help rangers and scientists to accelerate surveys and scale findings beyond a The official code for ConGeo. While part of this data is openly accessible to the public through Understanding ecosystems and their dynamics is essential for sustainable management and biodiversity conservation. At the Environmental Computational Science and Earth Observation Laboratory (ECEO), we extract knowledge from data that are heterogeneous and often very unstructured, Machine learning to understand the environment. For code-related We do not have any open positions at ECEO at the moment. Recent technologies — from widespread satellite imagery to AI A library to simplify the workflow of data acquisition from multiple satellite data providers - GitHub - eceo-epfl/earth-extractor: A library to simplify the workflow of data acquisition from mult “A Survey of Deep Active Learning,” ACM Comput. Generating insights on how and why the model comes to its own decision is valuable information Mapping wildlife-environment interactions in the Swiss Alps with AI In times when wildlife diversity is threatened by environmental changes, research Une chercheuse de l’EPFL montre comment nos forêts migrent en altitude Le Nouvelliste (2024) Une cartographie mondiale de la biodiversité forestière Il est donc nécessaire de développer les technologies pour organiser, cataloguer, rechercher et traiter les données. ch Baptiste We evaluate our approach in the task of ecosystem zero-shot classification by following the habitat definitions from the European Nature Information System (EUNIS). This EPFL-led initiative* served as a testing ground for DeepReefMap, an AI system developed at the Environmental Funding EPFL science seed fund Devis Tuia Associate Professor devis. New opportunities will be posted here as soon as they become available. ConGeo: Robust Cross-view Geo-localization across Ground View Variations Population maps are very important in several domains, like public health, urban planning and humanitarian projects. Contribute to eceo-epfl/ConGeo development by creating an account on GitHub. ch +41216938010 antoine. Au Laboratoire Li Mi li. ch +41 21 693 80 10 Natural Language Processing Lab EPFL Valais Wallis EPFL ENAC IIE ECEO Route des Ronquos 86 1951 Sion Therefore, this project aims at addressing these challenges and developing new AI models that are robust and effective in real-world scenarios for Devis Tuia's EPFL profileRecognition of outstanding services in the International Society for Photogrammetry and Remote Sensing (ISPRS) Code repository for the paper: The Coralscapes Dataset: Semantic Scene Understanding in Coral Reefs - eceo-epfl/coralscapesScripts Contributing If you are interested in contributing to one of the aforementioned points or working on a similar project and wish to collaborate, please reach out to ECEO. mi@epfl. Follow the steps below to get started: Download the Dataset: Visit the Zenodo page to download the dataset. The Environmental Computational Science and Earth Observation Laboratory (ECEO) at EPFL focuses on extracting patterns from Earth Observation data using machine learning. Project Student jan. ch +41 21 693 00 11 ALP 2 014 Antoine Bosselut Tenure Track Assistant Professor antoine. tuia@epfl. Similar in scope and with the same structure as the Large quantities of remote sensing data is being continuously produced from satellites. You may also explore other EPFL job openings. bosselut@epfl. While fine-grained population Equipe ‒ ECEO ‐ EPFL ECEO Coralscapes Dataset The Coralscapes dataset is the first general-purpose dense semantic segmentation dataset for coral reefs. This EPFL-led initiative* served as a testing ground for DeepReefMap, an AI system developed at the Environmental Watch on devis. Documentation (Coming soon): Check out the GitHub repository for detailed Official codebase for MammAlps. We develop ML methods for remote sensing, earth observation, ecology and conservation. ch . kokla@epfl. Contribute to eceo-epfl/MammAlps development by creating an account on GitHub. ch +41216930011 ALP 2 014 li. machraoui@epfl. ch Réza Bruno Boualem Machraoui Project Student reza. Environmental Computational Science and Earth Observation Laboratory has 16 repositories available. ch +41216936213 INR 234 Code repository for the paper: Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation - eceo-epfl/ScaleProtoSeg Environmental Computational Science and Earth Observation Laboratory has 16 repositories available. Follow their code on GitHub.
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