Earthling

Inspiration

It all started when I said it's impossible to have access to satellite data. I started my search and found plenty of open sources, either from space agency data or other online tools. First, I identified a disaster to work on, which is algae blooms. I chose it because of its riskiness as they are often instigated by pollution and changing temperatures and can kill a variety of marine and freshwater life through eutrophication.

What it does

Algal blooms affect coastal communities. I came up with a tracking mechanism to measure the presence of HAB. The use of RS products by a variety of available imagery NASA satellite data and other open sites and geospatial information would help address our challenge effectively. I noticed that chlorophyll is one major variable in algal blooms in the area; using (OLCI) system, I 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 to generate early warning signals for detected hazards. Finally, I used a visual interface ''GUI" to understand the impacts of the phenomenon.

How we built it

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. My 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

I 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. I 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. But to detect the disaster before happening, I 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. I processed the data by: Imagary Processing 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, I have achieved the part of image processing, data collection from the satellites. Now, I am working on the exact algorithm and trying to experiment it to show how accurate it could be.

Challenges we ran into

Finding NASA open resources to find the differences in colors of algae blooms.

Accomplishments that we're proud of

Working solely on imagery processing and collecting data from satellites.

What we learned

I learned how to use AI algorithms to process and detect hazards.

What's next for Earthling

Developing a more complicated algorithm to predict the hazard more quickly and more efficiently

Built With

• ai
• machine-learning
• prediction