Hi
user
Admin Login:
Username:
Password:
Name:
Detecting Road Conditions from Space Using PyTorch, Public Data, and Free Satellite Imagery
--client
pyohio
--show
pyohio_2025
--room cattleya 15564 --force
Next: 10 From Forge to Keyboard: Crafting a Developer’s Journey
show more...
Marks
Author(s):
Cynthia Ukawu
Location
Ballroom B
Date
jul Sat 26
Days Raw Files
Start
10:15
First Raw Start
10:59
Duration
00:30:00
Offset
0:44:55
End
10:45
Last Raw End
11:59
Chapters
00:00
0:18:37
0:25:41
Total cuts_time
31 min.
https://www.pyohio.org/2025/program/talks/detecting-road-conditions-from-space-using-pytorch-public-data
raw-playlist
raw-mp4-playlist
encoded-files-playlist
host
mp4
svg
png
assets
release.pdf
Detecting_Road_Conditions_from_Space_Using_PyTorch_Public_Data_and_Free_Satellite_Imagery.json
logs
Admin:
episode
episode list
cut list
raw files day
marks day
marks day
image_files
State:
---------
borked
edit
encode
push to queue
post
richard
review 1
email
review 2
make public
tweet
to-miror
conf
done
Locked:
clear this to unlock
Locked by:
user/process that locked.
Start:
initially scheduled time from master, adjusted to match reality
Duration:
length in hh:mm:ss
Name:
Video Title (shows in video search results)
Emails:
email(s) of the presenter(s)
Released:
has someone authorised pubication
Unknown
Yes
No
Normalise:
Channelcopy:
m=mono, 01=copy left to right, 10=right to left, 00=ignore.
Thumbnail:
filename.png
Description:
markdown
Monitoring the health of city streets and roadways is expensive, time-consuming, and often reactive. But what if we could automate part of that process using satellite imagery and Python? In this talk, we’ll walk through a real-world project that combines transfer learning, PyTorch, and publicly available datasets to classify road segment conditions (good, fair, poor) from aerial imagery. You'll learn how to work with messy real-world geospatial data, fine-tune a deep learning model using only a small training set (~2,000 examples), and overcome common challenges like blurry imagery, inconsistent labels, and overfitting. This session is practical and code-driven, aimed at data scientists and analysts working in mobility analytics, urban development, or infrastructure who want to apply machine perception techniques in their work. By the end, you’ll walk away with a reusable workflow for analyzing and predicting urban infrastructure quality — all using free tools and open data.
Comment:
production notes
2025-07-26/10_59_55.ts
Apply:
10:59:55 - 11:02:27 ( 00:02:32 )
S:
10:59:55 -
E:
11:29:54
D:
00:29:59
(
End:
152.0)
show more...
vlc ~/Videos/veyepar/pyohio/pyohio_2025/dv/cattleya/2025-07-26/10_59_55.ts :start-time=00.0 --audio-desync=0
Raw File
Cut List
10:59:55
seconds: 0.0
Wall: 10:59:55
Duration
00:29:59
11:29:54
seconds: 152.0
Wall: 11:02:27
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
2025-07-26/10_59_55.ts
Apply:
11:02:27 - 11:10:35 ( 00:08:08 )
S:
10:59:55 -
E:
11:29:54
D:
00:29:59
(
Start:
152.0) (
End:
640.0)
show more...
vlc ~/Videos/veyepar/pyohio/pyohio_2025/dv/cattleya/2025-07-26/10_59_55.ts :start-time=0152.0 --audio-desync=0
Raw File
Cut List
10:59:55
seconds: 152.0
Wall: 11:02:27
Duration
00:29:59
11:29:54
seconds: 640.0
Wall: 11:10:35
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
2025-07-26/10_59_55.ts
Apply:
11:10:35 - 11:11:17 ( 00:00:42 )
S:
10:59:55 -
E:
11:29:54
D:
00:29:59
(
Start:
640.0) (
End:
682.0)
show more...
vlc ~/Videos/veyepar/pyohio/pyohio_2025/dv/cattleya/2025-07-26/10_59_55.ts :start-time=0640.0 --audio-desync=0
Raw File
Cut List
10:59:55
seconds: 640.0
Wall: 11:10:35
Duration
00:29:59
11:29:54
seconds: 682.0
Wall: 11:11:17
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
2025-07-26/10_59_55.ts
Apply:
11:11:17 - 11:29:54 ( 00:18:37 )
S:
10:59:55 -
E:
11:29:54
D:
00:29:59
(
Start:
682.0)
show more...
vlc ~/Videos/veyepar/pyohio/pyohio_2025/dv/cattleya/2025-07-26/10_59_55.ts :start-time=0682.0 --audio-desync=0
Raw File
Cut List
10:59:55
seconds: 682.0
Wall: 11:11:17
Duration
00:29:59
11:29:54
seconds: 0.0
Wall: 10:59:55
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
2025-07-26/11_29_55.ts
Apply:
11:29:55 - 11:36:59 ( 00:07:04 )
S:
11:29:55 -
E:
11:59:55
D:
00:30:00
(
End:
424.0)
show more...
vlc ~/Videos/veyepar/pyohio/pyohio_2025/dv/cattleya/2025-07-26/11_29_55.ts :start-time=00.0 --audio-desync=0
Raw File
Cut List
11:29:55
seconds: 0.0
Wall: 11:29:55
Duration
00:30:00
11:59:55
seconds: 424.0
Wall: 11:36:59
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
2025-07-26/11_29_55.ts
Apply:
11:36:59 - 11:42:21 ( 00:05:22 )
S:
11:29:55 -
E:
11:59:55
D:
00:30:00
(
Start:
424.0) (
End:
746.0)
show more...
vlc ~/Videos/veyepar/pyohio/pyohio_2025/dv/cattleya/2025-07-26/11_29_55.ts :start-time=0424.0 --audio-desync=0
Raw File
Cut List
11:29:55
seconds: 424.0
Wall: 11:36:59
Duration
00:30:00
11:59:55
seconds: 746.0
Wall: 11:42:21
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
2025-07-26/11_29_55.ts
Apply:
11:42:21 - 11:57:01 ( 00:14:40 )
S:
11:29:55 -
E:
11:59:55
D:
00:30:00
(
Start:
746.0) (
End:
1626.0)
show more...
vlc ~/Videos/veyepar/pyohio/pyohio_2025/dv/cattleya/2025-07-26/11_29_55.ts :start-time=0746.0 --audio-desync=0
Raw File
Cut List
11:29:55
seconds: 746.0
Wall: 11:42:21
Duration
00:30:00
11:59:55
seconds: 1626.0
Wall: 11:57:01
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
2025-07-26/11_29_55.ts
Apply:
11:57:01 - 11:59:55 ( 00:02:54 )
S:
11:29:55 -
E:
11:59:55
D:
00:30:00
(
Start:
1626.0)
show more...
vlc ~/Videos/veyepar/pyohio/pyohio_2025/dv/cattleya/2025-07-26/11_29_55.ts :start-time=01626.0 --audio-desync=0
Raw File
Cut List
11:29:55
seconds: 1626.0
Wall: 11:57:01
Duration
00:30:00
11:59:55
seconds: 0.0
Wall: 11:29:55
Comments:
mp4
mp4.m3u
dv.m3u
Split:
Sequence:
:
delete
Rf filename:
root is .../show/dv/location/, example: 2013-03-13/13:13:30.dv
Sequence:
get this:
check and save to add this
2025-07-26/10_59_55.ts
2025-07-26/11_29_55.ts
Veyepar
Video Eyeball Processor and Review
