AI in Climate Change: Making the World a Better Place to Live
Climate Change is the concern of every person living on the planet earth, yet so far only a few of us have worked towards making a difference to the place we live in. A documentary by Netflix caught my attention. Titled: David Attenborough: A Life On Our Planet this feature documentary shares insights from the life of a naturalist and reflects on the key defining moments of his lifetime of observing devastating changes he has experienced.
What is more heartbreaking are the snapshots of global nature loss in a single lifetime. It harbors a powerful message of hope for future generations as the naturalist shares his thoughts on the possible solutions to save the planet from disaster. Let’s take a look:
We surely have advanced in technology, everything we do is becoming easier and easier thanks to IoT, Artificial Intelligence (AI), and Robotics. So is there any way these technologies combined can give us a solution to make a positive difference to the problem of climate change? Can AI give us hopes to curb the melting of glaciers and restore aqua-marine life that’s in danger of extinction? Can AI give us more sustainable solutions to live with and help in carbon footprint reduction?
There are so many more such questions that come to my mind, I am sure you have them too. So let’s get on a ride together in this blog and understand how can we get the best of AI technology we have and make this planet a better place to live.
Facts About Climate Change we Need to Know!
- In 2020 the concentration of carbon dioxide (CO2) has been the highest it has been in human history.
- The hottest temperature was recorded in 2020 which was more than 1.2C hotter than the 19th century.
- Eleven percent of all global greenhouse gas emissions caused by humans are due to deforestation
- Natural climate solutions such as restoring degraded forests could create as many as 39 jobs per million dollars spent but unfortunately, only 3% of the budget for climate change is allotted to this sector.
Now that we know how fast we are on the verge of losing our habitable environment and turn all brown like our neighbor Mars. We need to think about the AI-based solutions that can help us turn the table around.
AI in Climate Change
CO2 levels are the highest ever (COVID-19 has surely let this subsidize a bit), sea levels are rising by 3 inches in the last 25 years, at this rate most of the coastal areas are at the risk of getting submerged.
2020 was recorded to be the hottest year on record for the world’s oceans. AI is not a silver bullet but it can definitely help in reducing greenhouse gases that are depleting our planet.
According to Capgemini Research Institute modeling, AI is estimated to assist organizations in industries from consumer products to retail to automotive and more fulfill up to 45% of the Paris Agreement targets by 2030. AI will likely reduce GHG emissions by 16%.
Let’s check out the ways in which AI can help us save our planet.
Accurate Natural Disaster Predictions Using AI by NASA
Shortly before coming ashore in Louisiana, Hurricane Laura – pictured making landfall on Aug. 27 – underwent a process called rapid intensification, with winds that jumped 35 mph (56 kph) or more within 24 hours.
October 2015, Hurricane Patricia leaped from category 1 storm to category 5 monster within 24 hours. It suddenly strengthened itself in such a short time. Researchers led by NASA scientists at NASA’s Jet Propulsion Laboratory in Southern California have used machine learning to develop a computer model using machine learning that promises to improve accuracy in detecting rapid-intensification events.
NASA’s team added rainfall rate, ice water content, and temperature outflow predictors to the model and those machine learning models have to seem to be doing their job. The predictions are accurate and reliable. The computational algorithm capabilities of IBM Watson Studio have played a huge role in developing this machine learning model.
Intelligent Weather Forecasting by India Meteorological Department
We definitely dont want to land up in a situation like this. Accurate weather predictions are an important part of the smooth functioning of an economy. The emperor of all cricket leagues is the Indian Premier League. Just imagine your home team is in head-on competition with their fierce rival and it starts pouring during the match.
It’s a really disappointing situation and if you are a cricket fan (especially in India) you will relate better than anyone else.
Indian Meteorological Department is aiming to use AI in weather forecasting. Data from installed Radars and Satellite footages will be used to build a machine learning model for weather prediction.
Cornell University is researching on building a Machine Learning model for Precipitation Nowcasting from Radar Images. Typically the traditional weather forecasting was done using physical models of the atmosphere.
But these predictions were incertain to perturbations and hence were erroneous for big periods of time. This research will solve this problem and enable weather forecasters to publish updated weather forecasts in real-time!
Let’s pause here for a minute and watch a quick documentary highlight from Netflix!
Kiss the Ground! The documentary helps us reflect on the urgent need to maintain healthy soil. This message is consistently repeated throughout the entire documentary.
95% of farms have poorly managed soils with little vegetation and therefore a reduced ability to sequester and store carbon from the atmosphere.
Let’s figure out how can AI help us to have enriched soil conditions by keeping environmental predictions in mind.
AI for Accurate Predictions related to Agriculture
Agriculture-related AI-enabled forecasts like determining the best time to plant, use fertilizer, insecticide sprays, the proper time for irrigation, and the best time to harvest crops will help farmers around the globe to get the harvest of their dream.
According to IBM, 90 percent of crop losses are due to weather events and 25 percent of weather-related crop losses could be prevented by using predictive weather modeling.
The practice of monoculture is a thing of the past now with the help of AI farmers can identify the soil strength accurately coupled with accurate weather predictions and break the monoculture tradition (growing single crop on a large land)
Besides identifying the health of their land farmers can also manage their fertilization process based on the predicted weather. AI is a boon to this field, it helps the yield to improve by 3x as per the survey.
Smart Deforestation Tracking with AI
10% of global greenhouse-gas emissions contribute to deforestation. Scaling this length with manual efforts is a tedious and error-prone task. Using Satellite imagery and computer vision departments can automatically analyze the loss of tree cover at a greater scale.
Sensors on the ground can be combined with algorithms for detecting chainsaw sounds, can help local law enforcement stop illegal activity.
Data Labeling for Creating accurate NLP and CV models to Positively Impact Climate Change
Unstructured data that is collected to create AI models to curb climate change is also called unlabeled data and cannot be used for training a certain kind of AI-oriented model. The training data contains the communication within the humans on a particular topic and when they are annotated with Data Labeling the communications with humans becomes much easier.
There are multiple ways to get your training data annotated:
- Manual Data Labeling
- Crowdsourced Data Labeling
- Automated (Ai-powered) Data Labeling
Manual Data Annotation is not recommended because of the following reasons:
- Managing and maintaining the quality of data labeling
- Workforce management
- Keeping a track of the cost incurred
- Compliance with data privacy requirements
- The task to ensure data security
Crowdsourced Data Annotation has these challenges
- Managing data quality is difficult, most data labelers want to just finish the task and get their pay which does not negate the fact that your data might be mislabeled most of the time.
- Freelancers doing this task are typically unvetted people sitting at home and performing the task.
- Project Management gets daunting.
- The best way forward is adopting an Ai-powered data annotation tool that can eliminate all the challenges of the above two methods.
- Moreover, ai-powered data annotation tools are not expensive at all, they are a simple, reliable, and most efficient way to annotate your data.
Advantages of Adopting Smart Automated Data Annotator with Machine Learning
We have gained a fair understanding of why manual and crowdsourced data labeling is not a good choice to opt for if companies are looking to get an accurate, secured, and reliable data labeling task done.
Labellerr is a highly accurate data annotation platform with machine learning capabilities. The advantages of using such a platform are:
Data Scientists can achieve data labeling at scale and they can be free from the hassle of slow manual data labeling tasks. Especially with more and more Data Scientists adopting computer vision technology to maintain planogram compliance, autonomous checkouts, inventory stock detection, customer footfall detection, customer sentiment analysis, and many more such use-cases.
Data Scientists can get high-quality, reliable data labeling done. Unlike crowdsourcing platforms, data labeling companies hire long-term employees to oversee the task of data annotation. Thereby your data is in secure hands and the quality of output will be outstanding. Here are the key measures are taken by Labellerr to ensure your data is safe and secure:
- NDA signed with all the employees working on the data
- Prohibition of pen drives
- Video surveillance for monitoring
- Biometric systems to monitor the movement of employees
- ISO certification 27001:2013 and 9001:2015
Labellerr’s platform is deployed on the most secure and resilient platform like GCP, AWS and Azure
Well, here is another feature I believe you would love to explore, Labellerr is a one-stop-shop for all your data annotation tasks. It has a ML-powered data annotator platform and a marketplace. You can get data annotation tasks done by selecting a vendor.
Their data annotation platform is so robust and agile that anyone can annotate data without any support from the technical team.
It’s easy-breezy and the most comforting tool to get the task done – Check it out for yourself – Labellerr, an automated platform for image annotations and data labeling for AI and machine learning with the best quality and accuracy at flexible pricing.
For human-assisted ai-powered data annotation end-to-end solution check out their one-stop-shop marketplace and get-set-label!