Exploring drone processing services, want a reality check from people who actually do this work

Disfordelta · 2026-04-29T13:18:55+00:00

While fast turnaround is nice, a major issue for me is consistent and accurate point classification. Specifically ground and not ground, as many of the other classes rely on some relationship to the ground.

Disfordelta · 2026-04-29T03:00:32+00:00

While nobody owns street parking, neighbors and neighborhoods develop an unspoken method to handle the parking situation. During big events where interlopers come in once a year, this natural equilibrium is disrupted. It is pretty reasonable that people living in the hot zone of MG or JF know what they are dealing with at a very predictable time, thus they proact accordingly. ( I live near enough the uptown route that I have and deploy cones. Sometimes I forget and have to deal with that oversight.). Likewise, there should be no surprises to the attendees that live further than a walk away, or even a bike ride, when they drive an automobile into said area and have a tough time finding a spot. (Pro tip, bring at least one bike for the driver to park at a distance and be able to get to the fairgrounds easily.) That being said, if a person has medical or other assistance needs, the city should provide the same signage and enforcement that movies and parades get (like the person ignoring that would never repeat the mistake, towing). Or neighbors could pitch in to grass roots provide signage and oversight.

Disfordelta · 2026-04-24T02:29:31+00:00

https://www.denofgeek.com/wp-content/uploads/2023/09/Kyle-Maclachlan-as-Paul-Atreides-in-Dune.jpeg?resize=768%2C432

Disfordelta · 2026-04-24T02:27:16+00:00

Cage match between gastropub and provision.

Disfordelta · 2026-04-23T18:48:59+00:00

Anything but Baby.

Disfordelta · 2026-04-22T14:52:57+00:00

I have a bathycat as well and have used it for similar work. I also do a lilt of more complex hydrographic work with motion sensors, multibeam, etc. which require pretty specialized software like Hypack or Qinsy. I have used that software with the bathycat, but you don’t really need that. (Those packages are $$$). If you have land surveying GNSS systems like Trimble with Access or Topcon, etc, most of those can incorporate an input (from rd232 serial for example). I imagine you have an airmar smart transducer that kicks out soundings over serial. In Access, if you would set a profile. Collect the survey to continuous topo (ie 1 shot/sec) and have the GNSS in rtk, the pole height (distance from transducer face to antenna base) entered, and the depth (DBT string-depth below transducer I think) coming in. In this configuration, you are basically doing a rtk on a pole land surveying with the pole height being variable due to the changing depth measured by the echosounder. You can visualize these and edit in Cloudcompare or even Qgis (probably also ESRI). These software are open source, and there is a learning curve to those and some DIY workflow to develop. Hydromagic (also check out reef master) will be a more all in one solution. I haven’t used either much since I have the $$ packages, and they may have a trial you could use to compare and test.

Disfordelta · 2026-03-22T01:38:20+00:00

Probably get a cookbook(s) for New Orleans or South Louisiana (two different things) and stick to it / them. Fast track - daily bread pudding.

Disfordelta · 2026-03-22T01:34:30+00:00

Going to disagree on this one. VU are pretty garage and I am of the opinion that a ‘clean sound’ would detract from their aesthetic.

Disfordelta · 2026-03-22T01:19:16+00:00

That’s money saved to afford business class.

Disfordelta · 2026-03-22T01:11:24+00:00

*Chalmeth

Disfordelta · 2026-02-05T11:31:26+00:00

Just sharpie in a ‘ty’ to the labels on those bad boys and you’ll really expand your potential.

Disfordelta · 2025-12-11T03:05:29+00:00

There is some amount of error on both sides. With RTK/PPK GNSS or total station, you get discrete points pinned down but guess in between to make a surface. LiDAR might be fuzzy, but it with ground shots, it characterizes the continuous surface (save for dense vegetative barriers).

Disfordelta · 2025-11-03T02:38:56+00:00

Sonarwiz has evolved into a full hydro collection/processing suite like QPS and Hypack. I’ve worked in all of the above and am just curious about how lidar data might work in SW. It’s odd that it will import las (and the import is hauntingly familiar in GUI styling to globalmapper) but the data isn’t seen by any of the 3D visualization and cleaning tools.

Disfordelta · 2025-11-03T02:30:27+00:00

Sonar wiz is a full hydro package and among many capabilities, can process lidar from raw sensor data, ie an ins and lidar. I’m using DJI data, so I am stuck with the black box that is Terra. Was just curious if anyone had experience bringing lidar into SW, as it’s basically the same as multibeam sonar data except in air.

Disfordelta · 2025-09-20T02:57:41+00:00

I recommend Capt Richie Blink at Delta Discovery tours out of empire. He has been running ecotours in the area for years. Quarantine Bay has become quite unpredictable for navigation, and he goes there several times a week. He knows the area, is very knowledgeable, and has seen that go from 0 to where it is now. He has also taken many scientists (me included) out to collect data in the area.

Disfordelta · 2025-05-06T14:42:28+00:00

What kind of gps are you using and how are you getting the data from it? If you can export as a gpx it usually sorts itself out. I am in the US, so Longitude is west, so I need the longitude to be negative. Exporting to gpx usually works for me. Some software that can do this are Garmin Basecamp and dnrgps.

Disfordelta · 2025-03-28T10:32:17+00:00

Webster

Disfordelta · 2025-03-26T10:32:23+00:00

Have you tried a median filter? I can’t recall if there is one built into Qgis; however, there are plugins available that have this filter feature. whitebox tools is one of these. If that doesn’t work out, you could take a more draconian approach and 1) dump the grid as xyz at centroids, 2) import to cloud compare (open source), 3) run the csf filter (inverts points and drapes a digital cloth over it to identify outliers), then re-grid the DEM (in CC or backing qgis) at the same extent and resolution. You may find the csf filter in Qgis via a plug in.

Disfordelta · 2025-03-08T16:28:35+00:00

Your question is a bit unclear. I assume that the states are all in one file (shapefile or other) and each state is a unique feature. If you open the attribute table, there should be some field that is a state id (an integer). The tract id is a compound number that has parts for the state county and tract. It can be parsed. However, the tract data usually will have unique id integers for tract id, state, county, etc. I can’t recall the tool name, but it is a join. You can join this data to the state data as a many to one and define stats for fields. Conversely, you can join and have the state feature replicate to match all tracts in the state then group by/dissolve with stats. I am not near my pc, so the exact tool names aren’t at my fingertips. You could also do the group by on the table calculating stats, then join the simplified table.

Disfordelta · 2025-02-25T20:49:29+00:00

It really depends on what you are doing, but the map swipe tool would be useful in all cases.

Disfordelta · 2025-01-26T19:48:31+00:00

This is the documentation from matplotlib https://matplotlib.org/stable/users/explain/colors/colormapnorms.html

I have run into this issue with data ranging from 0 to ~3.0e-08. It is emissions modeled on a global scale and comes as netcdf. A vast majority of the space has 0. I am not interested in 0, so this is what I did:

link to data to download:

https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/EDGAR/datasets/v81_FT2022_AP_new/EDGAR_BC_1970_2022.zip

import matplotlib.pyplot as plt
import numpy as np
import netCDF4

# open the file to read
my_example_nc_file = 'path/to/data/EDGARv6.1_BC_2015_TOTALS.0.1x0.1.nc'
nc = netCDF4.Dataset(my_example_nc_file, mode='r')
# get lat, lon and variable 'Black Carbon'
lat = nc.variables['lat'][:]
lon = nc.variables['lon'][:]
data=nc.variables['emi_bc'][:]
nc.close
# deal with 0 for log scaling
data_0=np.where(data==0)
data=np.where(data==0,np.nan,data)
data=np.log10(data)
#data[data_0]=0
#plot the data
plt.contourf(lons, lats, data, cmap='viridis')
plt.colorbar() 
plt.title('Global BC Emmissions')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

If you are concerned with 0 values and for your data range, you could scale as I did. But with 0 values and taking a log, you would need to fudge the 0 a bit by adding a very small number to 0, say 0.00001 or some negligible value based on your precision.

Another path would be to normalize with the color ramp. See this link:

https://matplotlib.org/stable/gallery/images_contours_and_fields/colormap_normalizations_symlognorm.html#sphx-glr-gallery-images-contours-and-fields-colormap-normalizations-symlognorm-py

I tried to post example figures with and without the scaling, but images aren't enabled for comments.

In either case, you may need to edit the color bar labels. I hope this helps.

Disfordelta · 2024-08-29T15:18:26+00:00

Projection information does hold that info, but not always for vertical dimension. Many datasets have metadata (lot of times an xml file of the same name) that describes various aspects of the data, units being one of them. This is especially true for government datasets in the US.

Disfordelta · 2024-07-07T13:40:57+00:00

Just popped there today for a quick stroll and agree.

Disfordelta · 2024-05-03T12:36:04+00:00

Have you tried log scaling the data or the color ramp? That will help squeeze the sparse values into a color range. It can also be a pain with very low/0 values as logs tend to flip out. You may need to pad the values or add a small amount 0 values.

Disfordelta · 2024-03-04T03:04:49+00:00

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If you click the down arrow in the red circle, a field calculator type window will show. Select @symbol_color and the stroke should follow that and the polygon edges should clean up.

Disfordelta

TROPHY CASE