Top Tweets for #trainingdataset
Level up your #MachineLearning skills with the new RapidAI4EO #TrainingDataset, now accessible to the entire remote sensing community on Source Cooperative, our neutral data publishing utility! Click below to read more👇🏽
Today, through the #RapidAi4EO consortium, we released one of the largest Earth observation training datasets for #ML applications via @OurRadiantEarth.
The data covers 500,000 locations across Europe, captured every five days over two years. Learn more: https://t.co/wqaqtwsesB
🆕Upgrade your #ML skills w/ #RapidAI4EO! This #TrainingDataset has 500K locs across Europe, Sentinel2 mosaics & cloud-free @Planet data. It’s the largest collection of multimodal time series satellite imagery for various applications.
🙏🏽 Funded by 🇪🇺
🔗 https://t.co/RSAJqJxO5e
"Learn the ins and outs of a crucial aspect of ML: Training Datasets! Our CEO @Jesalthakkar14 of FutureBeeAI explains all in our latest blog: "All about Training Dataset in Machine Learning"
https://t.co/Ukf53hWnGM
#MachineLearning #TrainingDataset #FutureBeeAI"
🇦🇺Discover LandCoverNet for #Australia & #Oceania! It's part of the 1st global multi-satellite #TrainingDataset for land cover classification, created to help with high-res & up-to-date #geospatial maps for issues related to natural resource management
➡️https://t.co/EvrQKViNXO
Build your land cover classification model or validate its accuracy with LandCoverNet, a global benchmark #trainingdataset with satellite image pixels labeled for 7 land cover classes including water, permanent snow/ice, and much more. Gain open access at https://t.co/lxQKCkOxLx
🌊Did you know oceans cover ~70% of Earth? But marine pollution poses a threat to ocean life! If you are working on a monitoring system, check out the "Marine Debris Dataset for Object Detection" geospatial #trainingDataset for observing marine debris
🔗https://t.co/bOg4M3lIoP
Explore our latest #trainingdataset, AgriFieldNet Competition Dataset that we created with @IDinsight. It contains crop types of #AG fields in 4 states in 🇮🇳: Uttar Pradesh, Rajasthan, Odisha & Bihar. Access the data here👇 https://t.co/TNn26W7TLV
🚨#NewDataset🚨Explore this unique #trainingdataset of georeferenced crop images along with labels on input use, crop management, phenology, crop damage, and yields, collected across 8 counties in #Kenya. Gain open access on #RadiantMLHub https://t.co/li1FcofbyI
🌎Have you heard of LandCoverNet #NorthAmerica? Created from multi-satellite imagery this #trainingdataset has 7 land cover classes, labeled, which can help you create your own #geospatial map to help combat local and global issues. Gain open access at 👉 https://t.co/oooD80Qelz
Land cover is a key variable of #climatechange, which led @idiv & @UniHalle to create this benchmark crop type dataset for land cover classification to map the distribution of crops in Central Asia. Download this open-access #trainingdataset via: https://t.co/0CsCxXggWw
Build your #landcover classification model or validate its accuracy with LandCoverNet #Asia, a benchmark #trainingdataset with multi-satellite image pixels labeled for 7 land cover classes. Gain open access at ➡️https://t.co/S7P4Iusq7M
Learn about LandCoverNet Europe, a #TrainingDataset for #landcover classification with satellite images from #Sentinel1, #Sentinel2, + #Landsat8. Download now to create your own high-res & UpToDate #geospatial map for your natural resource mngt projects ➡️https://t.co/VrEXC3jnWq
🇦🇺Discover Radiant Earth's LandCoverNet #TrainingDataset for #Australia & #Oceania! Part of the 1st global multi-satellite dataset for land cover classification, it will help you create high-res & up-to-date #Geospatial maps to manage natural resources ➡️https://t.co/EvrQKVApmo
We recently announced the release of #LandCoverNet, the 1st global multi-satellite #TrainingDataset for land cover classification. This #TutorialTuesday, learn how how to use the #RadiantMLHub API to browse & download these datasets. See the link below 👇 https://t.co/qrvdIT4xfZ
🎉 We're excited to announce the release of LandCoverNet South America, a human-labeled #landcover classification #trainingdataset for the region.🌎
Now available for download on #RadiantMLHub
🔗https://t.co/VH65bzvdBA
🆕On #RadiantMLHub: Spatio-Temporal Deep Learning-Based Crop Classification Model for Satellite Data!
This pre-trained model classifies crops for small farms using data from our CV4A Kenya Crop Type Competition #trainingdataset. Created by Karim Amer.
🔗https://t.co/lxWUXZOD4p
New on #RadiantMLHub: A Spatio-Temporal Deep Learning-Based Crop Classification Model for Satellite Data!
This pre-trained model classifies crops for small farms using data from our CV4A Kenya Crop Type Competition #trainingdataset. Created by Karim Amer. https://t.co/lxWUXZOD4p
🌍 #TrainingDataset update!
We've republished LandCoverNet #Africa with the addition of Sentinel1 & Landsat8 source imagery, adding to the previous version that is based on Sentinel2!
Gain open access to this benchmark dataset via https://t.co/ak86DAzupo
🌍 #TrainingDataset update! We've republished LandCoverNet #Africa with the addition of Sentinel1 & Landsat8 source imagery, adding to the previous version that is based on Sentinel2!
Gain open access to this benchmark dataset via https://t.co/ak86DAzupo
📢 We are pleased to announce the release of “LandCoverNet,” a human-labeled global #landcover classification #trainingdataset for Africa.
🎉 Now available for download on Radiant MLHub, the open geospatial library
🔗 Learn more: https://t.co/FGXv62wwev
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