Earthlings| Automated Detection of Hazards
Awards & Nominations
Earthlings has received the following awards and nominations. Way to go!
Automated Detection of Hazards
Algal blooms tracker
Summary
Algal blooms affect the coastal communities.We came up with a tracking mechanism to measure the presence of HAB.The use of RS products by variety of available imagery NASA satellite data and other open sites and geospatial information would help address our challenge effectively.We noticed that chlorophyll is one major variable in algal blooms in the area;using (OLCI) system, we could capture signatures of biogeochemical that affect algal bloom growth.We have developed an algorithm using Ai to analyze and according to the presence of causing factors generate early warning signals for detected hazards.Finally, we used a visual interface ''GUI" to understand the impacts of the phenomenon.
How We Addressed This Challenge
There is a significant global increase in the number of disasters
Reducing and managing conditions of hazard, exposure and vulnerability that we can prevent losses and alleviate the impacts of disasters.
To solve our challenge we have taken specific, ordered scientific steps.
First we identified a disaster to work on, which is algae blooms.
We chose it because of its riskiness as it produces toxic and harmful effects on people, fish, shellfish, marine mammals, and birds, Also the human illnesses caused by HABs can be debilitating or even fatal. They affect the economy — especially coastal communities dependent on the income of jobs generated through fishing and tourism.
After that we studied the phenomenon in a detailed scientific study and studied its causes and the conditions that lead to its occurrence.
We consideredthat satellite remote sensing is a promising technique for studying HABs due to its advantages of large-scale, real-time, and long-term monitoring.
we collected data on each cause of the phenomenon from satellite data in NASA and other open sites and we did many researches until we get reliable results.
We also used gis, and this was a very important technique that helped us track and monitor the phenomenon.
Then we have developed an algorithm using artificial intelligence to analyze and generate early warning signals for detected hazards.
Finally we developed a visual interface ''web application '' to understand the impacts and scope of the phenomenon.
Measuring and anticipating the effects of future alagae blooms is important to implement measures to mitigate the effects of it and this is what we seek .
We have made a great effort and solved the challenge in order to protect the planet from this phenomenon. Our goal is lofty and noble. We dream of a planet free from disasters and this idea will certainly help in protecting people and protecting the economy from algal blooms.
How We Developed This Project
It all started when we said it's impossible to have access to satellite data.
We started our search and found plenty of open sources, either from space agency data or other online tools.
First, we identified a disaster to work on, which is algae blooms.
We chose it because of its riskiness as they are often instigated by pollution and changing temperature and can kill a variety of marine and freshwater life through eutrophication.
Usually these blooms give a distinct coloration visible in imagery, such as the red tide, although the coloration does vary depending on the type of bloom.
Given the importance of knowing how these blooms affect aquatic life, remote sensing techniques using a variety of available imagery have been developed. Variation in chlorophyll is one major variable in algal blooms in the area; using the Sentinel-3 Ocean and Land Color Instrument (OLCI) system, this instrument has been designed to capture signatures of biogeochemical that affect algal bloom growth.
Our idea can be divided into two parts:
The first part is the gathering of information using geographic information systems (GIS) - sensing from the distance, and also collecting sufficient information about the disaster, its causes, and everything about it.
The second part is developing an algorithm using artificial intelligence to analyze and generate early warning signals for detected hazards.
* GIS
The GIS will collect, save, retrieve, process, analyze, and display spatial data and information. It will produce maps, extract information, using software that conducts data management and analysis using DBMS software and designs, and displays data using AutoCAD.
* RS Remote Sensing
You will collect information about the phenomenon and this process is done using satellites, which occurs through an interaction between electromagnetic energy (the light source) and the phenomena of the surface of the earth to be photographed, and then it is necessary to use special imaging systems that can record the reflected energy.
We found a prior solution depending on the integrated ocean observing system, as satellites, buoys on the surface, and sensors on the ocean floor are collecting data on ocean color and currents, if algae blooms were predicted, scientists track them to estimate where they are travelling.
However, the question was:
How can we detect the disaster before happening?
Well, the past is an essential element in defining the future.
So, we decided to have a look at past images from satellites regards:
The fossil fuel factors, agricultural land, marine population and then collect statistics regards nitrogen and phosphorus presence in air, Lakes and Rivers, Coasts and Bay, Groundwater and Drinking Water.
Those factors play a crucial role in nutrient pollution which in turn cause algae blooms.
The further data we get, the most accurate we reach.
Our problem now is that we want to identify images from which
we take certain data. We can do this in two ways:
1-image Processing
2-machine learning.
We have chosen image processing.
Ok, beyond the image processing
We said other factors in it and said they will be outside our capabilities
This is the part regards the satellite, and when it was
covered, we found that for example, a special computer case is needed to
receive data from the satellite, and at the same time it can control it.
These data will be put in an algorithm in which several conditions will be tested and according to statistics it can estimate the predicted timing depending on the time it took to happen at past in presence of those factors.
Till now, we have achieved the part of image processing, data collection from the satellites.
Now, we are working on the exact algorithm and trying to experiment it to show how accurate it could be.
How We Used Space Agency Data in This Project
Our problem was to find a solution for the Algal blooms which is a kind of disaster that affects the marine life creatures. So, to detect the damages that are caused by the algal blooms we need to find a device that can inform us about changes that may happen on any point on earth, and this device was the satellites. We started searching for how can we get such a sort of data from the satellites, and it was found that there is a kind of PC that can receive the data from the satellite, and with the help of the space agency data, we can get much information. this information can then be checked by an algorithm which is called OpenCV. this algorithm is used to find the difference in the colors of the images that we have access to from the satellites.
Project Demo
https://www.slideshare.net/omarshehab9/algae-blooms-tracker
Data & Resources
- http://www.un-spider.org/links-and-resources/data-sources/daotm-HABs#HowMonitor
- https://sentinel.esa.int/web/sentinel/missions/sentinel-3/instrument-payload/olci
- https://worldview.earthdata.nasa.gov/
- https://www.sentinel-hub.com/spaceapps_challenge/
- https://github.com/GeekyPRAVEE/OpenCV-Projects/blob/master/LicensePlateRecoginition.ipynb
- https://pythonprogramming.net/loading-images-python-opencv-tutorial/#:~:text=OpenCV%20is%20used%20for%20all,through%20many%20Python%20examples%20here