July 24, 2018
PASCO CORPORATION[Press Release]"First Prize" in Satellite Image Recognition Competition ofBuilding Detection by AI/Machine Learning Technology
CVPR （IEEE Conference on Computer Vision and Pattern Recognition） 2018
DeepGlobe Satellite Challenge – Building Detection task
PASCO CORPORATION (Headquarters: Tokyo, Japan, President and CEO: Dr. Hideki
SHIMAMURA, hereinafter referred to as PASCO) provides the results by processing andanalyzing the geospatial information collected by satellites, aircraft, specialized vehicles,etc., as a geospatial information service for solving the social problems.
Especially for the services that utilize Earth Observation satellites, PASCO provides"Urban Change Analysis Map" and "Estimated Number of Parked Vehicles Map" inaddition to "Agricultural Land Use Survey Support Service" and "Surface Movement
Monitoring Service".
This time, for the building detection task in theDeepGlobe Satellite Challenge (an internationalcompetition) of “CVPR” which is the world's mostprestigious academic conference in the computer
vision research held in Salt Lake City, Utah, USA
in June 2018, the team of PASCO’s Satellite
Business Division achieved the highest accuracy
by utilizing company's proprietary AI/Machinelearning technology (deep learning technology)
and won the challenge.
(Ryuhei HAMAGUCHI & Shuhei HIKOSAKA, PASCO CORPORATION)■ Conference overview
Name: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Duration: June 18 – 22, 2018
Location: Salt Palace Convention Center, Salt Lake City, Utah, USA
URL： http://cpr2018.thecvf.com
CVPR is the world's most prestigious academic conferen ce in computer vision research,and in recent years, many state-of-the-art technologies related to image recognition using
Artificial Intelligence (AI) have been announced. At this conference, nearly 1000 papers
were published from all over the world, and over 5000 people participated.

－ 1/2 －■ Conference overview
Name: DEEPGLOBE CVPR 2018 - Satellite Challenge
URL: http://deepglobe.org/leaderboard.html
The competition took place for recognition accuracy in satellite imagery interpretation
for three different tasks, building detection (389 teams), road extraction (454 teams) andland cover classification (400 teams). PASCO team won the 1st place in building detectiontask.
■ Paper summary
In the recent years, convolutional neural networks (CNNs) show remarkably highperformance in building detection tasks. Many previous studies have been conducted sofar but it was not considered sufficiently about how to address a wide variation in buildin gsize, and how efficiently to incorporate with context information such as roads. On theother hand, in this research, we proposed a multitask model that can handle differencesin size of buildings appropriately by using specialized detectors for each siz e of buildings.
In addition, the model implicitly utilizes context information by simultaneously training
road extraction task along with building detection task.
Our experiment showed that theproposed model can greatly improvethe detection accuracy of the building.
The reason that the PASCO teamwon the challenge was the method ofhandling buildings which is patentapplied according to size, and such an
approach was not found among themethods of other participants, and it
was unique among all participants.
http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/
w4/Hamaguchi_Building_Detection_From_CVPR_2018_pap er.pdf■ Utilization of results
PASCO will utilize this result for building change interpretations, international
statistical estimation, updates of forecast map, etc., to improve the quality of services weoffer to solve society's problems, and also for t he company’s internal productivity.
■ Contact
PASCO CORPORATION

https://www.pasco.co.jp/eng/

Public Relations Department

mail: webmaster@pasco.co.jp

Satellite Business Division

TEL: +81 3-5465-7370－ 2/2 －