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231 results for “developmentseed”
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stac-map: A map-centric interface for searching and visualizing SpatioTemporal Asset Catalog (STAC) data.
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County-to-county migration in the U.S.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://developmentseed.org/lonboard/v0.4.0/examples/migration/
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County-to-county migration in the U.S.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://developmentseed.org/lonboard/v0.4.0/examples/migration/
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County-to-county migration in the U.S.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://developmentseed.org/lonboard/v0.4.0/examples/migration/
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County-to-county migration in the U.S.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://developmentseed.org/lonboard/v0.4.0/examples/migration/
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County-to-county migration in the U.S.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://developmentseed.org/lonboard/v0.4.0/examples/migration/
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Rivers of Asia, with width and color based on their centrality.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/6-asia.ipynb
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Rivers of Asia, with width and color based on their centrality.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/6-asia.ipynb
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Rivers of Asia, with width and color based on their centrality.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/6-asia.ipynb
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Rivers of Asia, with width and color based on their centrality.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/6-asia.ipynb
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Rivers of Asia, with width and color based on their centrality.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/6-asia.ipynb
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DevSeed has now integrated DS-Annotate with the Segment Anything Model. This powerful tool enables swift and efficient annotation of map features, streamlining our processes significantly. This advancement wouldn't have been possible without our collaborative work with Ruben Mendoza, Ryan Avery, Wille Marcel, and Nick Ingalls.
#Geospatial #AI #Mapping #OSM #Sentinel2 #DevelopmentSeed #DSAnnotate #SegmentAnything #DataScience #osm #annotation #machinelearning #labeling #datateam #Meta #SAM #IA
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DevSeed crew is headed to #sotmus2023 this week. Lots of stuff to share and excited to connect with OSM folks in person.
https://developmentseed.org/blog/2023-06-06-see-you-at-sotmus23 -
A must-read: "Beyond Open Data" (https://cloudnativegeo.org/beyond-open-data-white-paper.pdf) explains how “open license + URL” isn’t enough. Three quotes worth noting: (1) usefulness of data is more important than just branding it as "open", (2) publishing datasets without maintaining the relationships between them does not produce an effective ecosystem (invest in the governance, the identifiers, the accountability), (3) treat "the ecosystem", not "the dataset", as the thing worth maintaining. @developmentseed @cloudnativegeo
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deck.gl-raster added a COG comparison slider example. Drag to compare two raster image sources, rendered entirely client-side with no backend needed. Useful pattern for before/after imagery or switching between datasets in a map UI.
https://developmentseed.org/deck.gl-raster/examples/vermont-cog-comparison/
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deck.gl-raster added a COG comparison slider example. Drag to compare two raster image sources, rendered entirely client-side with no backend needed. Useful pattern for before/after imagery or switching between datasets in a map UI.
https://developmentseed.org/deck.gl-raster/examples/vermont-cog-comparison/
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deck.gl-raster added a COG comparison slider example. Drag to compare two raster image sources, rendered entirely client-side with no backend needed. Useful pattern for before/after imagery or switching between datasets in a map UI.
https://developmentseed.org/deck.gl-raster/examples/vermont-cog-comparison/
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deck.gl-raster added a COG comparison slider example. Drag to compare two raster image sources, rendered entirely client-side with no backend needed. Useful pattern for before/after imagery or switching between datasets in a map UI.
https://developmentseed.org/deck.gl-raster/examples/vermont-cog-comparison/
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deck.gl-raster added a COG comparison slider example. Drag to compare two raster image sources, rendered entirely client-side with no backend needed. Useful pattern for before/after imagery or switching between datasets in a map UI.
https://developmentseed.org/deck.gl-raster/examples/vermont-cog-comparison/
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A year of vehicle collisions in NYC, with radius determined by number of pedestrians and cyclists injured, and colored by time of day.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/1-points.ipynb
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A year of vehicle collisions in NYC, with radius determined by number of pedestrians and cyclists injured, and colored by time of day.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/1-points.ipynb
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A year of vehicle collisions in NYC, with radius determined by number of pedestrians and cyclists injured, and colored by time of day.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/1-points.ipynb
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A year of vehicle collisions in NYC, with radius determined by number of pedestrians and cyclists injured, and colored by time of day.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/1-points.ipynb
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A year of vehicle collisions in NYC, with radius determined by number of pedestrians and cyclists injured, and colored by time of day.
Made in Python with #lonboard. #30DayMapChallenge
Reproduce it with this notebook: https://github.com/developmentseed/lonboard/blob/main/examples/map_challenge/1-points.ipynb
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DevSeed has now integrated DS-Annotate with the Segment Anything Model. This powerful tool enables swift and efficient annotation of map features, streamlining our processes significantly. This advancement wouldn't have been possible without our collaborative work with Ruben Mendoza, Ryan Avery, Wille Marcel, and Nick Ingalls.
#Geospatial #AI #Mapping #OSM #Sentinel2 #DevelopmentSeed #DSAnnotate #SegmentAnything #DataScience #osm #annotation #machinelearning #labeling #datateam #Meta #SAM #IA
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Help! @bothness and I are looking to upgrade the #FOSS infrastructure of #PalOpenMaps with a newer version of #OSM-seed.
Last time, I did the deploy, and learned #docker to do it. Now the infrastructure orchestration is done using #kubernetes, and I have neither the desire or the patience to deploy it myself.
We have a relatively small budget to pay a devops person to deploy for us. Any takers?
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What can a tree do when the Earth's temperature rises and the trees cannot relocate?
Excited to share a project we built called 𝑶𝒖𝒓 𝑭𝒐𝒓𝒆𝒔𝒕𝒔 𝑻𝒐𝒎𝒐𝒓𝒓𝒐𝒘.
Explore these interactive maps and data to see where our forests might head in a warming world. #ForestDay
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🌐 We're a global team, and while virtual meetings are great, nothing beats face-to-face interactions. But how do we meet responsibly with less environmental impact?
#ghg #co2 #cop28 #climatechange #airtravel #remotework #remoteteams -
🌐 We're a global team, and while virtual meetings are great, nothing beats face-to-face interactions. But how do we meet responsibly with less environmental impact?
#ghg #co2 #cop28 #climatechange #airtravel #remotework #remoteteams -
🌐 We're a global team, and while virtual meetings are great, nothing beats face-to-face interactions. But how do we meet responsibly with less environmental impact?
#ghg #co2 #cop28 #climatechange #airtravel #remotework #remoteteams