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  • Writer's pictureArpit Shah

Geo-Analytics for Damage Assessment

Updated: Aug 22, 2021

Assessing the impact of natural or man-made disasters on operations is vital from a risk management and business continuity point of view. Prevention is always better - as elaborated in this hurricane risk case for insurance industry. However, these incidents are often un-controllable and need to be assessed (as elaborated in oil spill and forest fire use cases) and responded to. The impact of pause in operations at a particular node / location is often magnified at other nodes across the supply chain. The Thailand floods of 2011 is a perfect example - auto, electronics and several other global supply chains were heavily affected. I distinctly remember there was a phase where it was difficult to obtain a brand new computer hard drive in Mumbai in the aftermath of this incident.

Ignore the 'bullwhip' effect of such situations at your own peril!

In this article, I will highlight two examples of damage assessment using geo-analytics. The first is assessing the impact of the horrific explosion in the port city of Beirut, Lebanon in 2020 and the second involves detecting a phenomenon which is seemingly innocuous but very damaging in its own way - Road Cracks.

Both the damage assessments have been done using different ways - the former via analysing optical and satellite imagery whereas the latter involves the usage of Deep Learning on images captured from a vehicle's dashcam.


Example 1 : Assessing Beirut Blast Damage

Final Output: Damage Assessment - Beirut Explosion - 4th August 2020

Without delving into the methodology of deriving this output, essentially I've taken two radar satellite images prior to the explosion (July 2020) and derived its coherence i.e. similarity / correlation between the pixels of the two images. Then, I derived the coherence of another two images - one image before the explosion and one after the explosion. The difference between the two coherence outputs is what is depicted in the image above.

Note: The image above also contains the damage extent as determined by analysing optical satellite imagery - one image prior to and another post the explosion. However, it is largely masked by the damage extent as determined by analysing the radar images. This is due to the fact that the properties of radar imagery make it more capable at capturing land surface changes information than optical imagery.

Damage Output (Red) as detected by analysing pre and post explosion optical satellite imagery

The Beirut explosion is one of the largest non-nuclear explosion till date. While loss to life was restricted to the vicinity of the explosion site, loss to property occurred even tens of kilometres away. The shockwaves of the explosion was felt in adjacent countries (>250 kms)! With this disaster, the already fledgling economy of Lebanon was put in further turmoil.

Much thanks to ESA & RUS Copernicus for the training material and imagery analysis methodology.

Do read this article if you'd like to know more about what satellite imagery analytics entails.


Example 2 : Detecting Road Cracks using Deep Learning

Do watch the demonstration of this amazing geo-solution from the video below -

Road Crack Detection using DL demo video. Methodology can be found here.

The demo was conducted by my firm's GIS technology partner - Esri at its developer conference in 2019. I had the good fortune of seeing this demo live later at Esri User Conference in Kolkata in 2019. It was presented by Mr. Divyansh Jha - Data Scientist at Esri and the DL methodology was applied on Vehicle Dashcam images captured on the Delhi - Faridabad highway route. Basis my interaction with Divyansh - I gathered that the entire assessment process lasted a month - from capturing data to analysing it and validating the results.

From both time and cost perspective - geo-technology blended with AI, ML, DL & IoT can be very beneficial. With the advent of 5G telecommunications, I do envisage these becoming mainstream and being able to solve a multitude of pressing problems faced by organizations and institutions worldwide.

I have personally used Esri DL models to detect new objects - buildings and swimming pools - from aerial images. The output was quite impressive - left me mesmerized.


Intelloc Mapping Services | is engaged in selling products which capture geo-data (Drones), process geo-data (Geographic Information System) as well as services (PoI Datasets & Satellite Imagery). Together, these help organizations to benefit from Geo-Intelligence for purposes such as operations improvement, project management and digital enabled growth.

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