Analyzing LiDAR and SAR data with Capella Space and TileDB
For a summary and to access example notebooks, see the blog post at https://tiledb.com/blog/analyzing-lidar-and-sar-data-with-capella-space-and-tiledb/. We were pleased to feature Jason Brown, Remote Sensing Image Scientist and Community Enablement Engineer at Capella Space (https://www.capellaspace.com/), as a guest speaker in our webinar. Also, a special thanks to Hobu, Inc. (https://hobu.co/) for providing the open-source LAS data set used in these examples. *About the presentation* Thanks to advances in remote sensing, earth observation and geospatial analytics are awash with new data sources. Combining them, however, can be a big challenge, especially wrangling data for machine learning workflows. TileDB Cloud eliminates data wrangling, with a canonical array data format and a single API that lets geospatial scientists work efficiently with both dense SAR imagery and sparse point cloud data at massive scale. In this presentation, in collaboration with Capella Space, engineers from both companies will cover technical examples featuring analysis-ready data modeled as multi-dimensional arrays, concluding in a data fusion example where open SAR imagery is combined with labeled point cloud data, suitable for ML and advanced visualization techniques. In this presentation of SAR data analytics on TileDB Cloud, you will learn how to: • Directly compute on cloud-based 2D dense arrays of Capella Space SAR imagery • Use your favorite tools via hosted geospatial notebook environments and customize them with specific Python libraries, like PCL, PDAL, Rasterio, and Scikit-Learn • Leverage TileDB arrays to visualize your own cloud-native time-series cubes of Capella Space SAR images • Slice data from different TileDB Cloud arrays and fuse the results in Jupyter notebooks for advanced analyses • Discover and scale collaborative analyses through TileDB Cloud data sharing, without large downloads and complex IAM hassles *Contents of this video* 0:00:00 – An introduction to TileDB 0:14:19 – TileDB in Geospatial 0:19:54 – Jason Brown on Capella Space and SAR 0:27:01 – TileDB Cloud refresher 0:28:58 – SAR ingest & LiDAR point overlap 0:32:57 – LiDAR ingest & visualization 0:34:21 – Export into COG 0:35:25 – Colorizing point data with SAR data 0:38:15 – SAR & LiDAR data visualization 0:39:44 – Inferred point classifications onto SAR scenes 0:42:02 – Q&A *About* TileDB makes data management and compute fast, easy and universal. Manage any data as multi-dimensional arrays and access with any tool at global scale. *Connect with us* Website: https://tiledb.com/ Twitter: https://twitter.com/tiledb LinkedIn: https://www.linkedin.com/company/tiledb-inc/ Book a personalized product demo: https://tiledb.com/demo Sign up at https://cloud.tiledb.com/auth/signup and contact hello@tiledb.com for free credits.
For a summary and to access example notebooks, see the blog post at https://tiledb.com/blog/analyzing-lidar-and-sar-data-with-capella-space-and-tiledb/. We were pleased to feature Jason Brown, Remote Sensing Image Scientist and Community Enablement Engineer at Capella Space (https://www.capellaspace.com/), as a guest speaker in our webinar. Also, a special thanks to Hobu, Inc. (https://hobu.co/) for providing the open-source LAS data set used in these examples. *About the presentation* Thanks to advances in remote sensing, earth observation and geospatial analytics are awash with new data sources. Combining them, however, can be a big challenge, especially wrangling data for machine learning workflows. TileDB Cloud eliminates data wrangling, with a canonical array data format and a single API that lets geospatial scientists work efficiently with both dense SAR imagery and sparse point cloud data at massive scale. In this presentation, in collaboration with Capella Space, engineers from both companies will cover technical examples featuring analysis-ready data modeled as multi-dimensional arrays, concluding in a data fusion example where open SAR imagery is combined with labeled point cloud data, suitable for ML and advanced visualization techniques. In this presentation of SAR data analytics on TileDB Cloud, you will learn how to: • Directly compute on cloud-based 2D dense arrays of Capella Space SAR imagery • Use your favorite tools via hosted geospatial notebook environments and customize them with specific Python libraries, like PCL, PDAL, Rasterio, and Scikit-Learn • Leverage TileDB arrays to visualize your own cloud-native time-series cubes of Capella Space SAR images • Slice data from different TileDB Cloud arrays and fuse the results in Jupyter notebooks for advanced analyses • Discover and scale collaborative analyses through TileDB Cloud data sharing, without large downloads and complex IAM hassles *Contents of this video* 0:00:00 – An introduction to TileDB 0:14:19 – TileDB in Geospatial 0:19:54 – Jason Brown on Capella Space and SAR 0:27:01 – TileDB Cloud refresher 0:28:58 – SAR ingest & LiDAR point overlap 0:32:57 – LiDAR ingest & visualization 0:34:21 – Export into COG 0:35:25 – Colorizing point data with SAR data 0:38:15 – SAR & LiDAR data visualization 0:39:44 – Inferred point classifications onto SAR scenes 0:42:02 – Q&A *About* TileDB makes data management and compute fast, easy and universal. Manage any data as multi-dimensional arrays and access with any tool at global scale. *Connect with us* Website: https://tiledb.com/ Twitter: https://twitter.com/tiledb LinkedIn: https://www.linkedin.com/company/tiledb-inc/ Book a personalized product demo: https://tiledb.com/demo Sign up at https://cloud.tiledb.com/auth/signup and contact hello@tiledb.com for free credits.