Southern Taiwan Stars

https://github.com/love3499/NASAHackathon-2022-Southern-Taiwan-Stars

https://youtu.be/-hoJjo5ZOn0

You are welcome to click the link below to view our project demo.

Please use the Chrome Browser to open the demo link: http://nasahackthon.thebestyea.net

Service detail

Our Active Inspection Platform based on Quake Hunter will redefine the maintenance process of bridges and roads, and provide a tripartite platform for residents, Inspection company, and the public agency.

Here are the platforms and services provided by each party:

1. All people can participate in reporting obstacles or conditions of the road and obtain road safety information and maintenance progress through our open-source platform.

2. With the ability to integrate satellite information in real-time through AI, the platform will be able to proactively predict potential disaster areas and alert the public agency, so that the public agency can take action in a timely manner.

3. When the inspection and maintenance company receives the maintenance task dispatched by the public agency, with 3D modeling images captured by drones, only a few manpower are required for going on-site. Through the bridge and road inspection model on the platform, we can quickly find the location of defects and assist the decision-making procedure for the Engineering unit.

4. The damage information and maintenance progress of various highways and bridges will be updated on the platform in real-time. Public agency and road users will be immediately alerted via the platform.

Technical detail

The platform we built based on ” Quake Hunter” includes three modules: Hidden Abnormal Road Inspection Module, UAV Road Inspection Module, and Open Inspection Platform for Abnormal Road Area. The hidden danger area inspection module is designed to quickly locate hidden dangerous bridges and roads based on satellite data and assist the public agent in quickly formulating management and maintenance strategies. UAV Road Inspection Module completes the automatic bridge defect inspection process and outputs a predicted inspection report for maintenance personnel to confirm the damage status of the bridge. Finally, we integrate our platform with NASA's Earth data visualization website open source. Via the open source website from NASA, the residents can check the maintenance results or road conditions at any time.

Our Active Inspection Platform based on Quake Hunter will redefine the maintenance process of bridges and roads, and provide a tripartite platform for residents, manufacturers, and the government.

The satellite cloud images and disaster information provided on the NASA Worldview and NASA SEDAC websites inspired us to innovate the bridge and road maintenance workflow. therefore we built a platform based on Quake Hunter. our aim is to improve the efficiency of the emergency response functions of regional natural disasters and road maintenance for the public agency. At the same time, we can also facilitate the road maintenance of mountainous areas. In this project, these satellite cloud images and the information from various disasters were adopted to map our own satellite cloud images with information regarding earthquakes, mudslides, and rainfall. The image will be input to the unsupervised learning and active detection model, enabling the model to analyze the risk in each area. Besides, Data Mining technology was applied to construct a model that will give alarms of anomaly value, and rise warning information (eg, the exceeding information of regional rainfall). Finally, the information would be presented on the 'Quake Hunter' via our platform.

In the future, the data collected in this project will also be published on the platform as a reference for other inspection teams with the hope of providing other UAV inspection workers with standardized procedures to create an open data of Active Inspection Platform for Abnormal Road Area Based on Quake Hunter.

The team has been committed to research and development for the combination of UAV and AI image recognition technology for high-altitude inspection and other related applications. In the past, the bridge and road inspection system we developed can only assess the degree of damage to bridges and roads through regular inspections. It is difficult to find the abnormal structure of the bridges and roads in advance.

Inspired by the NASA hackathon competition and NASA Open data, our team focused more on improving the sustainable development and disaster resilience of urban infrastructure.

Our team proposes a new type of bridge and road inspection method, hoping to quickly locate bridges and roads with safety concerns after a disaster. finally, we incorporate the built platform with NASA's data visualization opensource that can contribute to the sustainable development of the city.

[1] AlexeyAB/darknetPaper Yolo v4: https://arxiv.org/abs/2004.10934 More details: medium link Discussion: Reddit About Darknet framework…github.com

[2]https://worldwind.arc.nasa.gov/quakehunter/

[3] https://heartbeat.comet.ml/deep-learning-for-image-segmentation-u-net-architecture-ff17f6e4c1cf

[4] https://worldview.earthdata.nasa.gov/

[5] https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4

[6] https://sedac.ciesin.columbia.edu/data/set/ndh-earthquake-frequency-distribution/maps/services

[7] D. Cavaliere, V. Loia, A. Saggese, S. Senatore and M. Vento, "Semantically Enhanced UAVs to Increase the Aerial Scene Understanding," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 3, pp. 555-567, March 2019, doi: 10.1109/TSMC.2017.2757462.

[8] Y. Qiming, Z. Jiandong and S. Guoqing, "Modeling of UAV path planning based on IMM under POMDP framework," in Journal of Systems Engineering and Electronics, vol. 30, no. 3, pp. 545-554, June 2019, doi: 10.21629/JSEE.2019.03.12.

#Road Inspection, #Deep Learning, #Unsupervised Learning, #Quake Hunter, #open source Earth data visualization web application