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Rafagas LinksOmniCloudMask is a Python library for state-of-the-art segmentation of clouds and cloud shadows in high- to moderate-resolution satellite imagery <a href="https://en.osm.town/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a><a href="https://github.com/DPIRD-DMA/OmniCloudMask" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/DPIRD-DMA/OmniCloudMask</a>

Fabrizio MusacchioTried the same with a more realistic 3D stack from the <a href="https://sigmoid.social/tags/ImageJ" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ImageJ</a> sample library.<a href="https://sigmoid.social/tags/Cellpose" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Cellpose</a> runs fast and segments very well out of the box.<a href="https://sigmoid.social/tags/CellSeg3D" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#CellSeg3D</a> takes considerably longer and seems to segment decently, but I couldn’t get a proper instance <a href="https://sigmoid.social/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a> in the post-processing step (which is recommended as part of its workflow). However, <a href="https://sigmoid.social/tags/CellSeg3D" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#CellSeg3D</a> looks very promising — just needs some more time and parameter exploration, I guess. I’d recommend giving it a try 👌

Fabrizio Musacchio✍️ New in <a href="https://sigmoid.social/tags/eLife" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#eLife</a>: <a href="https://sigmoid.social/tags/CellSeg3D" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#CellSeg3D</a> introduces <a href="https://sigmoid.social/tags/WNet3D" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#WNet3D</a>, a self-supervised 3D <a href="https://sigmoid.social/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a> method for <a href="https://sigmoid.social/tags/microscopy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#microscopy</a> data — no labels needed. Claims to outperform <a href="https://sigmoid.social/tags/Cellpose" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Cellpose</a>/#StarDist on 4 datasets. Includes <a href="https://sigmoid.social/tags/opensource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#opensource</a> plugin (<a href="https://sigmoid.social/tags/Napari" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Napari</a>) + full 3D annotated <a href="https://sigmoid.social/tags/cortex" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#cortex</a> dataset. Will test it later. 🌍 <a href="https://elifesciences.org/articles/99848" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://elifesciences.org/articles/99848</a><a href="https://sigmoid.social/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DeepLearning</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Neuroscience</a>

Rafagas LinksSemantic segmentation of historical maps using self-constructing graph convolutional networks to interpret and extract information <a href="https://en.osm.town/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a><a href="https://www.tandfonline.com/doi/full/10.1080/15230406.2025.2468304" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.tandfonline.com/doi/full/10.1080/15230406.2025.2468304</a>

nf-corePipeline release! nf-core/molkart v1.1.0 - 1.1.0 - Resolution Road!Please see the changelog: <a href="https://github.com/nf-core/molkart/releases/tag/1.1.0" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/nf-core/molkart/releases/tag/1.1.0</a><a href="https://mstdn.science/tags/fish" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#fish</a> <a href="https://mstdn.science/tags/imageprocessing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#imageprocessing</a> <a href="https://mstdn.science/tags/imaging" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#imaging</a> <a href="https://mstdn.science/tags/molecularcartography" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#molecularcartography</a> <a href="https://mstdn.science/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a> <a href="https://mstdn.science/tags/singlecell" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#singlecell</a> <a href="https://mstdn.science/tags/spatial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#spatial</a> <a href="https://mstdn.science/tags/transcriptomics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#transcriptomics</a> <a href="https://mstdn.science/tags/nfcore" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#nfcore</a> <a href="https://mstdn.science/tags/openscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#openscience</a> <a href="https://mstdn.science/tags/nextflow" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#nextflow</a> <a href="https://mstdn.science/tags/bioinformatics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#bioinformatics</a>

feitgemelHow to segment X-Ray lungs using U-Net and TensorflowYou can find link for the code in the blog : <a href="https://eranfeit.net/how-to-segment-x-ray-lungs-using-u-net-and-tensorflow/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://eranfeit.net/how-to-segment-x-ray-lungs-using-u-net-and-tensorflow/</a>Check out our tutorial here : <a href="https://youtu.be/-AejMcdeOOM&list=UULFTiWJJhaH6BviSWKLJUM9sg" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://youtu.be/-AejMcdeOOM&list=UULFTiWJJhaH6BviSWKLJUM9sg</a>Enjoy Eran<a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Python</a> <a href="https://mastodon.social/tags/openCV" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#openCV</a> <a href="https://mastodon.social/tags/TensorFlow" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#TensorFlow</a> <a href="https://mastodon.social/tags/ImageSegmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ImageSegmentation</a> <a href="https://mastodon.social/tags/Unet" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Unet</a> <a href="https://mastodon.social/tags/Resunet" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Resunet</a> <a href="https://mastodon.social/tags/Segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Segmentation</a>

💧🌏 Greg CocksSegment Anything Model Can Not Segment Anything - Assessing AI Foundation Model’s Generalizability In Permafrost Mapping -- <a href="https://doi.org/10.3390/rs16050797" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://doi.org/10.3390/rs16050797</a> <-- shared paper -- <a href="https://techhub.social/tags/GIS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GIS</a> <a href="https://techhub.social/tags/spatial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#spatial</a> <a href="https://techhub.social/tags/mapping" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#mapping</a> <a href="https://techhub.social/tags/remotesensing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#remotesensing</a> <a href="https://techhub.social/tags/foundationmodel" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#foundationmodel</a> <a href="https://techhub.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://techhub.social/tags/artificialintelligence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#artificialintelligence</a> <a href="https://techhub.social/tags/zeroshot" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#zeroshot</a> <a href="https://techhub.social/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a> <a href="https://techhub.social/tags/GeoAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GeoAI</a> <a href="https://techhub.social/tags/spatialanalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#spatialanalysis</a> <a href="https://techhub.social/tags/LargeLanguageModel" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LargeLanguageModel</a> <a href="https://techhub.social/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a> <a href="https://techhub.social/tags/SAM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#SAM</a> <a href="https://techhub.social/tags/performance" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#performance</a> <a href="https://techhub.social/tags/metrics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#metrics</a> <a href="https://techhub.social/tags/permafrost" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#permafrost</a> <a href="https://techhub.social/tags/visionmodel" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#visionmodel</a> <a href="https://techhub.social/tags/icewedge" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#icewedge</a> <a href="https://techhub.social/tags/Arctic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Arctic</a> <a href="https://techhub.social/tags/warming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#warming</a> <a href="https://techhub.social/tags/climatechange" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#climatechange</a> <a href="https://techhub.social/tags/thawslumps" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#thawslumps</a> <a href="https://techhub.social/tags/landform" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#landform</a> <a href="https://techhub.social/tags/terrainmapping" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#terrainmapping</a> <a href="https://techhub.social/tags/EuroCrops" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EuroCrops</a> <a href="https://techhub.social/tags/agriculture" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#agriculture</a>

ALTAIn the lead up to <a href="https://sigmoid.social/tags/ALTA2024" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ALTA2024</a>, we're highlighting <a href="https://sigmoid.social/tags/research" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#research</a> papers from previous <a href="https://sigmoid.social/tags/workshops" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#workshops</a>. Here, the ChatGPT C-LARA-Instance, Belinda Chiera, Cathy Chua, Chadi Raheb, Manny Rayner, Annika Simonsen, Zhengkang Xiang, and Rina Zviel-Girshin use the <a href="https://sigmoid.social/tags/OpenSource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#OpenSource</a> <a href="https://sigmoid.social/tags/CLARA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#CLARA</a> platform to evaluate <a href="https://sigmoid.social/tags/GPT4" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GPT4</a>'s ability to perform <a href="https://sigmoid.social/tags/linguistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#linguistics</a> <a href="https://sigmoid.social/tags/NLP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#NLP</a> tasks such as <a href="https://sigmoid.social/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a>, <a href="https://sigmoid.social/tags/lemmatization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#lemmatization</a> and <a href="https://sigmoid.social/tags/glossing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#glossing</a>. 🔗 C-LARA platform: <a href="https://www.c-lara.org/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.c-lara.org/</a>🔗 Paper: <a href="https://aclanthology.org/2023.alta-1.3/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://aclanthology.org/2023.alta-1.3/</a>

Rafagas LinksDetectTree is a Python library that uses semantic segmentation to classify whether pixels in aerial images contain a tree. The library includes a pre-trained model via martibosch at X <a href="https://en.osm.town/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a><a href="https://github.com/martibosch/detectree" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/martibosch/detectree</a>

JCLSNot on novels, but on Holocaust survivor testimonials: Eitan Wagner, Renana Keydar, Amir Pinchevski & Omri Abend (2023). "Automatic Topic-Guided Segmentation of Holocaust Survivor Testimonies", Journal of Computational Literary Studies 2 (1), 1–26. doi: <a href="https://doi.org/10.48694/jcls.3580" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://doi.org/10.48694/jcls.3580</a>. Keywords: <a href="https://fedihum.org/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a>, <a href="https://fedihum.org/tags/spoken" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#spoken</a> narratives, <a href="https://fedihum.org/tags/testimonies" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#testimonies</a>, <a href="https://fedihum.org/tags/narrative" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#narrative</a> analysis, <a href="https://fedihum.org/tags/topic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#topic</a> analysis, mutual information, <a href="https://fedihum.org/tags/NLP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#NLP</a> <a href="https://fedihum.org/tags/CLS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#CLS</a> <a href="https://fedihum.org/tags/JCLS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#JCLS</a>

Robin WilsonHave you wanted to use segment-geospatial (<a href="https://samgeo.gishub.org/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://samgeo.gishub.org/</a>) on Microsoft Planetary Computer and struggled? My blog post shows the simple solution: <a href="https://blog.rtwilson.com/how-to-get-segment-geospatial-working-on-microsoft-planetary-computer/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://blog.rtwilson.com/how-to-get-segment-geospatial-working-on-microsoft-planetary-computer/</a><a href="https://mastodon.me.uk/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#python</a> <a href="https://mastodon.me.uk/tags/ml" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ml</a> <a href="https://mastodon.me.uk/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a> <a href="https://mastodon.me.uk/tags/PlanetaryComputer" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PlanetaryComputer</a> <a href="https://mastodon.me.uk/tags/geospatial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#geospatial</a> <a href="https://mastodon.me.uk/tags/gis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#gis</a>

Sally Lowell... further to the above, if anyone would like to explain in 500 characters or less, why a caterpillar has more than six legs then I'll grade their micro-essay for them.<a href="https://biologists.social/tags/DevBio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DevBio</a> <a href="https://biologists.social/tags/Entomology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Entomology</a> <a href="https://biologists.social/tags/Segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Segmentation</a> <a href="https://biologists.social/tags/Friday" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Friday</a>

Michel MarianiUnicopedia Plus is a developer-oriented set of Unicode, Unihan, Unikemet & emoji utilities wrapped into one single app, built with <a href="https://mastodon.social/tags/Electron" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Electron</a>.Repository: 🔗 <a href="https://codeberg.org/tonton-pixel/unicopedia-plus" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://codeberg.org/tonton-pixel/unicopedia-plus</a><a href="https://mastodon.social/tags/characters" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#characters</a> <a href="https://mastodon.social/tags/chinese" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#chinese</a> <a href="https://mastodon.social/tags/cjk" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#cjk</a> <a href="https://mastodon.social/tags/codepoints" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#codepoints</a> <a href="https://mastodon.social/tags/desktopapplication" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#desktopapplication</a> <a href="https://mastodon.social/tags/electronjs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#electronjs</a> <a href="https://mastodon.social/tags/emoji" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#emoji</a> <a href="https://mastodon.social/tags/ivd" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ivd</a> <a href="https://mastodon.social/tags/japanese" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#japanese</a> <a href="https://mastodon.social/tags/javascript" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#javascript</a> <a href="https://mastodon.social/tags/kangxi" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#kangxi</a> <a href="https://mastodon.social/tags/kangxiradicals" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#kangxiradicals</a> <a href="https://mastodon.social/tags/korean" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#korean</a> <a href="https://mastodon.social/tags/normalization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#normalization</a> <a href="https://mastodon.social/tags/opensource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#opensource</a> <a href="https://mastodon.social/tags/regex" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#regex</a> <a href="https://mastodon.social/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a> <a href="https://mastodon.social/tags/strokecount" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#strokecount</a> <a href="https://mastodon.social/tags/unicode" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#unicode</a> <a href="https://mastodon.social/tags/unicopedia" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#unicopedia</a> <a href="https://mastodon.social/tags/unihan" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#unihan</a> <a href="https://mastodon.social/tags/unikemet" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#unikemet</a>

Bill Taroli :neurodiversity:I've been on the search for a <a href="https://federate.social/tags/mesh" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#mesh</a> <a href="https://federate.social/tags/WiFi" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#WiFi</a> option that supports multiple SSID associated to tagged <a href="https://federate.social/tags/VLAN" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#VLAN</a>, which span into the LAN. I do this now with <a href="https://federate.social/tags/FreshTomato" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#FreshTomato</a> to my <a href="https://federate.social/tags/pfSense" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#pfSense</a> router. But I need better WiFi coverage. So far, it's looking like <a href="https://federate.social/tags/OrbiPro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#OrbiPro</a> and <a href="https://federate.social/tags/DrayTek" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DrayTek</a> devices do this, but one isn't high on my expectations list and one isn't easily acquired in the US. So I'm trying the Orbi Pro and we'll see...<a href="https://federate.social/tags/HomeNetworking" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#HomeNetworking</a> <a href="https://federate.social/tags/security" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#security</a> <a href="https://federate.social/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a>

Classe débrouillards<a href="https://mastodon.twictee.org/tags/Transfert" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Transfert</a> <a href="https://mastodon.twictee.org/tags/Twict%C3%A9e" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Twictée</a> <a href="https://mastodon.twictee.org/tags/Majuscule" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Majuscule</a> <a href="https://mastodon.twictee.org/tags/Segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Segmentation</a> <a href="https://mastodon.twictee.org/tags/Ponctuation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Ponctuation</a> Bravo la <a href="https://mastodon.twictee.org/tags/TeamCP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#TeamCP</a>

Niklas AltHi fediverse, is anyone aware of <a href="https://fedihum.org/tags/opensource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#opensource</a> pipelines for <a href="https://fedihum.org/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a> / <a href="https://fedihum.org/tags/vectorization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#vectorization</a> of <a href="https://fedihum.org/tags/historical" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#historical</a> <a href="https://fedihum.org/tags/cadastral" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#cadastral</a> maps? Ideally a workflow to train <a href="https://fedihum.org/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> / <a href="https://fedihum.org/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ML</a> models on specific mapsets, e.g. the new prussian survey after 1870*, the francisceian (mid 19th century) or the bavarian*** to only mention the largest surveys in central Europe. I suspect that people outside of history are working on it, these maps are a true treasure for <a href="https://fedihum.org/tags/environmental" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#environmental</a> and <a href="https://fedihum.org/tags/biodiversity" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#biodiversity</a> research. Links are in the reply

Oliver MantellA new role with the University of Leicester (in partnership with us at The Audience Agency) looking at how <a href="https://zirk.us/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> can support cultural <a href="https://zirk.us/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a>: <a href="https://jobs.le.ac.uk/vacancies/8889/data-scientist-for-cultural-audience-segmentation-ktp-associate.html" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://jobs.le.ac.uk/vacancies/8889/data-scientist-for-cultural-audience-segmentation-ktp-associate.html</a>It is, of course, potentially a huge can of worms (!), but I'm hoping it will enable greater nuance, flexibility, learning and local/sector specificity - as well as more robust evidence of what works... (and done with partners who place social value and impact first).

Anita Graser 🇪🇺🇺🇦🇬🇪RT <a href="https://fosstodon.org/@giswqs" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@giswqs</a> segment-geospatial v0.6.0 sneak peek - Interactive <a href="https://fosstodon.org/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a> of <a href="https://fosstodon.org/tags/remotesensing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#remotesensing</a> <a href="https://fosstodon.org/tags/imagery" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#imagery</a> without coding. Segmentation results can be saved as GeoTIFF and vector data formats<a href="https://github.com/opengeos/segment-geospatial/pull/44" rel="nofollow noopener noreferrer" target="_blank">https://github.com/opengeos/segment-geospatial/pull/44</a><a href="https://fosstodon.org/tags/geospatial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#geospatial</a> <a href="https://fosstodon.org/tags/segmentanything" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentanything</a> <a href="https://fosstodon.org/tags/eo" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#eo</a> <a href="https://fosstodon.org/tags/gischat" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#gischat</a>

Rafagas LinksSegment Anything Model (SAM) is a fast system to classify any object from any image without previous training automatically, and it works reasonably well on aerial imagery <a href="https://en.osm.town/tags/segmentation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#segmentation</a><a href="https://segment-anything.com/" rel="nofollow noopener noreferrer" target="_blank">https://segment-anything.com/</a>

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