Hello Tamas,
Nice to meet you, and congratulations on your tool. It has been very
motivating for our work.
We have a private repository on Github, and I'll immediately invite you
to give access. We do not want to make it public yet, because we are
waiting for a publication, although we hope it will be soon.
El 18-4-23 a las 09:30, Tamas Szekeres escribió:
Luis,
I'm interested in testing such algorithm, is that code publicly available?
Best regards,
Tamas
Luis Felipe Romero <fel...@uma.es> ezt írta (időpont: 2023. ápr. 18.,
K, 7:15):
Dear gdal-dev group members,
I hope this email finds you well. I am Luis Felipe Romero, a
researcher from the University of Malaga in Spain, and I am
excited to introduce myself to this community. My research focuses
on algorithms for digital elevation models (DEM), and in
particular, the development of algorithms for the calculation of
the total viewshed.
Recently, my team has developed a highly efficient algorithm that
can calculate the total viewshed for a model of 2500x2500 points
in just 4 seconds on a GPU or approximately 2 minutes on a 16-core
CPU. Our algorithm is written in C++, and we also have a version
that utilizes CUDA for GPU acceleration.
I had a conversation with Even Roualt about the possibility of
integrating our algorithm into GDAL. However, he informed me of
the incompatibilities between GDAL and CUDA, but encouraged me to
share my intentions with the group. Therefore, I would like to
share our Total Viewshed approach with the gdal-dev community.
Unlike Tamas Szekeres' Viewshed algorithm, our approach calculates
the viewshed at all model points simultaneously by leveraging
in-memory data alignment. While it may not be as efficient for
calculating the viewshed of a small set of points, it is
incredibly useful for determining the viewshed of an entire
territory, such as for selecting the location of a cell phone
tower or for forest surveillance path planning. Our algorithm can
complete these types of calculations in a matter of minutes, as
opposed to thousands of hours.
My primary goal in reaching out to this group is to gain feedback
and insights on how GDAL can be used to make our algorithms
publicly accessible and even improved upon by the community. I
believe that GDAL provides the necessary tools and interfaces to
make this possible.
I am currently in the process of familiarizing myself with the
GDAL code structure, and it will take a few days before I can
determine my first steps with it. Nevertheless, I am excited to
contribute to this community and give visibility to our work.
Thank you all in advance for your time and consideration.
Best regards,
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