Timeline for Automated detection of tracks
Current License: CC BY-SA 3.0
15 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 12, 2015 at 13:27 | history | edited | ragnvald |
Added feature extraction tag
|
|
| Jan 7, 2015 at 7:41 | comment | added | ragnvald | Simbamangu, I have added your comment on automation. I only want to know where the tracks are. When that is known the matter of aggregating to areas is rather simple. Any relief from manual processing is good, that is the main goal for finding a relevant method. Searches online has shown that there are several ways to do this. But I am looking for an existing production line which can be modified towards my needs. | |
| Jan 7, 2015 at 7:37 | history | edited | ragnvald | CC BY-SA 3.0 |
Added reflections around "full automation" as suggested by simbamangu.
|
| Jan 5, 2015 at 14:36 | comment | added | Simbamangu | Ragnvald, very interesting question - would be interested to know (perhaps click 'edit' and update your original question): (1) do you want a purely automated system for detection? (2) Do you want to actually map the area of the tracks, or just know if they are there (presence / absence)? | |
| Jan 3, 2015 at 17:14 | answer | added | superifa | timeline score: 4 | |
| Dec 19, 2014 at 11:22 | history | edited | ragnvald | CC BY-SA 3.0 |
Added sample imagery
|
| Dec 18, 2014 at 3:26 | comment | added | Michael Stimson | I should qualify my previous statement, if you've got to acquire LiDAR and multispectral imagery for this project and can't offset the cost (co-purchasing with another company or government department that has separate interests in the same area) the cost is going to be high, probably higher than capture, provided you can source labor at a low rate. I'm not saying it's impossible, far from it, it would be very interesting as a white paper, just probably not the cheapest option. | |
| Dec 18, 2014 at 0:31 | comment | added | Michael Stimson | I'm not sure about LiDAR on snow (don't get much of that in Queensland), open spaces are a lot easier to classify and you can get away with cheaper sensors (less returns per pulse).. in heavily vegetated areas it is imperative to use multiple returns per pulse to find the ground; the pulse density is the same but less returns. To find your furrows your point spacing will have to be quite fine (in excess of 8 pulses/sq.m.) which means lower flying, more strips, more cost, more storage, longer processing time. It could be a lot cheaper to get capture from photography done in China or India. | |
| Dec 17, 2014 at 23:30 | history | tweeted | twitter.com/#!/StackGIS/status/545360407452737537 | ||
| Dec 17, 2014 at 23:29 | comment | added | ragnvald | Michael, thank you for your inputs! The parks are likely to be marches and mossy open areas. But - if the procedures could be applied on savannas then that's fine too. Snow is certainly an alternative - and then related to snow mobiles. A too broad scope (like including snow) would probably not benefit the question. I kept it generic to attract more potential answers. A "final" answer should probably cover as many of the options as possible. | |
| Dec 17, 2014 at 23:17 | comment | added | Michael Stimson | What are the parks likely to be? Desert, Savannah, Tundra, Ice & snow, Tropical Rainforest? | |
| Dec 17, 2014 at 22:55 | comment | added | ragnvald | Thanks Aron, yes it is related to off-road tracks. We aim at documenting them to get an indication of the extent of any damage. We would probably limit it to some management unit like nature reserves, national parks or similar. Per now we are trying to figure out our options before designing a project. | |
| Dec 17, 2014 at 22:48 | comment | added | Aaron♦ | I assume your ultimate goal is to identify off-road tracks. What is the extent of your study area? Where is the study area? | |
| Dec 17, 2014 at 22:27 | comment | added | Michael Stimson | That's a good question. Usually I prefer two platforms - one to find, one to confirm. A depression that's brown is more likely to be a wheel rut, conversely non-wheel depressions are more likely to be 'green'. LiDAR will give you the best DEM to find depressions and IR is the best at classifying vegetation/not vegetated. Be aware that LiDAR, depending on many factors, will give different results where water has pooled. Traditionally these would be captured from photography alone, which is a lot of hours work, but you have to weigh up the cost of labor vs data acquisition. | |
| Dec 17, 2014 at 22:19 | history | asked | ragnvald | CC BY-SA 3.0 |