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NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



Easily Share Workflows With the Analytics Repository

Easily Share Workflows With the Analytics Repository

10/27/2025

With the recent release of ENVI® 6.2 and the Analytics Repository, it’s now easier than ever to create and share image processing workflows across your organization. With that in mind, we wrote this blog to: Introduce the Analytics Repository Describe how you can use ENVI’s interactive workflows to... Read More >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... Read More >

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

10/13/2025

On July 24, 2025, a unique international partnership of SaraniaSat, NV5 Geospatial Software, BruhnBruhn Innovation (BBI), Netnod, and Hewlett Packard Enterprise (HPE) achieved something unprecedented: a true demonstration of cloud-native computing onboard the International Space Station (ISS) (Fig. 1). Figure 1. Hewlett... Read More >

NV5 at ESA’s Living Planet Symposium 2025

NV5 at ESA’s Living Planet Symposium 2025

9/16/2025

We recently presented three cutting-edge research posters at the ESA Living Planet Symposium 2025 in Vienna, showcasing how NV5 technology and the ENVI® Ecosystem support innovation across ocean monitoring, mineral exploration, and disaster management. Explore each topic below and access the full posters to learn... Read More >

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

9/8/2025

Geohazards such as slope instability, erosion, settlement, or seepage pose ongoing risks to critical infrastructure. Roads, railways, pipelines, and utility corridors are especially vulnerable to these natural and human-influenced processes, which can evolve silently until sudden failure occurs. Traditional ground surveys provide only periodic... Read More >

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Native OPeNDAP support in IDL

Anonym

OPeNDAP is an acronym for "Open-source Project for a Network Data Access Protocol." From the OPeNDAP website: “OPeNDAP provides software which makes local data accessible to remote locations regardless of local storage format.” Through Unidata’s netCDF-4 library, IDL 8.2 includes native support for OPeNDAP, though currently only on Mac OS X and Linux. Joe Lee of The HDF Group has created an example of using OPeNDAP with a community-generated IDL client. I’d like to modify (and slightly expand) his example to instead use the native OPeNDAP support in IDL 8.2. Start with the URL to an AIRS level 3 standard daily product file (HDF-EOS format) hosted on The HDF Group’s OPeNDAP-enabled server:

 IDL> url = 'http://eosdap.hdfgroup.uiuc.edu:8080/opendap/data/NASAFILES/hdf4/AIRS.2008.10.27.L3.RetStd001.v5.2.2.0.G08303124144.hdf' 

Access the file at this URL using netCDF library calls. Open the file with NCDF_OPEN, which returns an identifier (a long integer) for the file:

 IDL> file_id = ncdf_open(url)

What’s in this file? Use NCDF_INQUIRE to find the number of variables, dimensions and attributes:

 IDL> info = ncdf_inquire(file_id) IDL> help, info ** Structure <85778ac>, 4 tags, length=16, data length=16, refs=1: NDIMS           LONG                26 NVARS           LONG               274 NGATTS          LONG                 0 RECDIM          LONG                -1

There are 274 variables in this file. Use NCDF_VARIDSINQ to get identifiers for all the variables:

 IDL> var_ids = ncdf_varidsinq(file_id) IDL> help, var_ids VAR_IDS         LONG      = Array[274]

Use NCDF_VARINQ to get information about the last variable in the file:

 IDL> help, ncdf_varinq(file_id, var_ids[-1]) ** Structure <85779ec>, 5 tags, length=40, data length=40, refs=1: NAME            STRING    'TopographyU274' DATATYPE        STRING    'FLOAT' NDIMS           LONG                 2 NATTS           LONG                 3 DIM             LONG      Array[2]

Extract data from this last variable in the remote file to your IDL session. Use NCDF_VARID to get an identifier for the variable, by name (note that this string is case-sensitive), and NCDF_VARGET to actually get the data, which will be stored locally in the variable topo.

 IDL> topo_id = ncdf_varid(file_id, 'TopographyU274') IDL> ncdf_varget, file_id, topo_id, topo IDL> help, topo TOPO            FLOAT     = Array[360, 180]

We’ve successfully pulled data from the remote file into IDL! To properly visualize these data, also get their geographic coordinates:

 IDL> latitude_id = ncdf_varid(file_id, 'LatitudeU271') IDL> longitude_id = ncdf_varid(file_id, 'LongitudeU272') IDL> ncdf_varget, file_id, latitude_id, latitude IDL> ncdf_varget, file_id, longitude_id, longitude IDL> help, longitude, latitude LONGITUDE       FLOAT     = Array[360] LATITUDE        FLOAT     = Array[180]

Then close the file:

 IDL> ncdf_close, file_id

Visualize these topography data as a contour plot in a map projection:

 IDL> m = map('Miller Cylindrical', $ > color='gray', $ > label_color='black', $ > label_position=0, $ ; move labels to edges > title='AIRS.2008.10.27.L3 Topography') IDL> nlevels = 11 IDL> levels = findgen(nlevels)*500 + 250 ; meters IDL> g = contour(topo, longitude, latitude, $ > overplot=m, $ >   c_value=levels, $ >   rgb_table=33, $ >   background_color='dark blue', $ >   /fill, $ >   grid_units='degrees') IDL> c = mapcontinents(color='white') IDL> cb = colorbar(target=g, $ >   position=[0.2, 0.13, 0.85, 0.15], $ >   title='Elevation (m)')

and save the visualization to a PNG file:

 IDL> p.save, 'idl_opendap_ex.png', resolution=150 

The result (click to embiggen): AIRS topography retrieved via OPeNDAP with IDL Grab the source code for this example from here.

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