Rapid Response Monitoring Tools



With rapid data access, new vehicles and tools that can go almost anywhere (e.g., such as unmanned aerial vehicles), and more efficient computing, we are witnessing an age of rapid response monitoring that addresses environmental and social problems as they develop. In many different areas, tools are being developed so that professionals and researchers have access to a combination of data delivery platforms, data analysis, and accurate location data.

Rapid Response Monitoring Tools for Monitoring the Great Barrier Reef

Such developments are exemplified in recent work done by the Australian Institute for Marine Science (AIMS) and Queensland University of Technology that have used UAVs, hyperspectral cameras and automated machine learning classification to train computers to automatically classify coral reef bleaching, a particularly major problem affecting areas such as the Great Barrier Reef in Australia. The Great Barrier Reef is about the size of Japan and difficult to monitor using traditional methods. Automated methods of data capture and classification make it possible for the effort to also be highly cost effective.[1]

Using Land-based Robots to Monitor Penguin Colonies

Furthermore, more than just UAVs, we are now seeing a variety of robotic machines used along with different sensors, although most of these sensors are forms of cameras that capture visible or invisible parts of the spectrum. Areas where cyborg monitoring tools have been used or could be used in the near future include the bloodstream, desert regions, caves, underwater, and in small areas such wall cracks using robotic insects. One remote region is Antarctica, where using normal aerial drones could be difficult in cloudy conditions; however, land-based robots have now allow for automated monitoring of Emperor penguins in their breeding grounds. Even robots with mobile labs now allow automatic sequencing of genes to be performed in the field, where blood samples can be taken and analyzed instantly so that the conditions of biological populations are also better understood, such as new viruses or disease that could affect a population.[2]

Figure: Grémillet et al., 2012.

Automate Monitoring in Agriculture

Not only monitoring wildlife and natural resources has become more automated but agricultural businesses have greatly benefit from automated monitoring. Integrating thermal and multispectral cameras with UAVs allow nearly real-time monitoring that look at subtle conditions that can affect crops, where monitoring plant morphology, canopy volume and leaf area index are some of the types of data captured and that provide up to the minute developments affecting crop areas.[3]

Figure: Das et al., 2015.

UAVs in Urban Areas

In urban regions, UAVs have been used to monitor traffic and building construction. In fact, using automated image capture with computer vision model reconstruction, building sites can be modeled in 3D and 4D in real time. These models can be also provided to people on the ground who then provide further input through their own observations of buildings such as through text updates. These multiple data inputs could also be streamlined where UAVs can inform on the ground robots to monitor any areas for potential structural issues, such as cracks after earthquakes. Machine learning, including natural language processing, could also be used to incorporate textual or unstructured data as part of real-time monitoring.[4] Researchers also see that more work needs to be done to improve computer vision techniques that can even anticipate problems in building structures and safety by monitoring real-time images. For instance, subtle changes to a part of a building or even how machines are configured on a working site could forecast if an accident could occur.[5]

Future of Rapid Response Tools

In reality, almost anywhere we can set a camera or sensor then that area can be interlinked with automated tools such as machine learning classification to inform on the results of the data. Future areas of development include new forms of robots and even combined land and air systems that could navigate uneven terrain or varying topographies better than current tools. Thus, much of the future work is envisioned to focus on new platforms to obtain more real-time data.[6] Sensor technologies could also improve more in areas such as monitoring behind enclosed spaces using thermal imaging and robotic touch using artificial skin or surfaces to better understand organisms’ or objects’ conditions in real-time.[7] Machine learning that uses memory from previous events and can anticipate how changes could have potential consequences on site could make monitoring also forecast and not simply responding to current conditions, as seen from the building monitoring example.

Figure: Montero et al., 2015.

Overall, the last decade has witnessed a revolution in automated monitoring technologies and techniques. What the future holds is even better tools that can monitor more difficult to reach places inside and outside organisms, large bodies, and over wide areas.


[1] For more on the approach in a recent news item, see:  https://www.qut.edu.au/news/news?news-id=122198

[2] For more on how robots are playing a greater role in automated monitoring, see: Grémillet, D., Puech, W., Garçon, V., Boulinier, T., et al. (2012) Robots in Ecology: Welcome to the machine. Open Journal of Ecology. [Online] 2 (2), 49–57. Available from: doi:10.4236/oje.2012.22006.

[3] For more on the use of automated and rapid monitoring techniques for crops, see: Das, J., Cross, G., Qu, C., Makineni, A., et al. (2015) Devices, systems, and methods for automated monitoring enabling precision agriculture. In: [Online]. August 2015 IEEE. pp. 462–469.

[4] For more on the integration of data capture from UAVs, on-the-ground personnel and other devices, along with computer vision techniques for building monitoring, see:  Ham, Y., Han, K.K., Lin, J.J. & Golparvar-Fard, M. (2016) Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works. Visualization in Engineering. [Online] 4 (1).


[5] For more on computer vision techniques for monitoring and forecasting, see: Yang, J., Park, M.-W., Vela, P.A. & Golparvar-Fard, M. (2015) Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future. Advanced Engineering Informatics. [Online] 29 (2), 211–224.

[6] One article where the future of data gathering techniques is discussed in monitoring can be found here:  Montero, R., Victores, J.G., Martínez, S., Jardón, A., et al. (2015) Past, present and future of robotic tunnel inspection. Automation in Construction. [Online] 59, 99–112.

[7] For more on future sensor technologies, see:  Bartolozzi, C., Natale, L., Nori, F. & Metta, G. (2016) Robots with a sense of touch. Nature Materials. [Online] 15 (9), 921–925.

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