Philippines relief map at Rizal Park, Manila

Another approach I do is: 1. Download features from overpass. 2. Load the features to JOSM’s Todo list. 3. Start resolving items while the “Download OSM data continuously” is active. This makes sure you have the latest data each time you fix and item in the todo list.

Sa uulitin Leigh!

❤️🚪

Thanks Ron! I too enjoyed all the scholars’ lightning talks.

This is very cool, looking forward to the next phase!

We had a chance to test RapiD via the https://tasks-assisted.hotosm.org/ at Pista ng Mapa 2019 conference in Dumaguete, Philippines last week. The general comments of the local community is positive and has the potential of improving the speed and quality of mapping (tweet of the demonstration here).

A couple of notes below:

1. Unlike other parts of the world, detections in the Philippines are fewer, this is because most areas (population centers) were already well mapped by the local community. The detections provided by RapiD allowed us to identify the last remaining gaps to complete the road coverage.
2. Geometry quality of detections are very good (although jot perfect) and is comparable to an average mapper. This allows us to focus on tagging quality and connectivity instead of tracing. The time to investigating the correct tag based on local knowledge augmented by imagery will definitely improve the overall quality of the data.
3. Validation is integrated to the workflow. Most common geometry errors can be avoided before upload.

We are continuing the tests and we will share back observations to FB to improve the workflow. Good job to those who built it!

How did you prepare the subset with rs subset to set it up for 80/10/10?

I found something on the net that splits a text file by line based on %, so basically you input your cover csv file to this script: https://gist.github.com/maning/8a381a66e4f245429d67a6f39e205e24

Yay new release! I definitely give the docker build a try!

❤️

@tomas straupis,

Is there a difference in trained model prediction results from epochs 20-50?

Good idea I have not tested the other high IoUs, I’ll report back here of the result.

@NewSource

can you please share the source 660c5321-0334-471f-bca5-829d85fb1d40.tif ?

Sure, here

by the way what the way used to assemble several tiffs together ?

I mentioned it here:

Since I have to do this in all individual imagery, I created a script that loop through each imagery list from OpenAerialMap.

http://oin-astrodigital.s3.amazonaws.com/LC81230392015234LGN00_bands_432.TIF

It looks like you are downloading landsat imagery, is this what you want to download? Depending on the zoom level you download, this is expected to be low resolution (28m pixel resolution).