“Managing, communicating, and collaborating with the Omdena teams helped me significantly to get my job at Microsoft”
Analyzing Land Conflicts and Government Policies through Natural Language Processing
- The Results
Project completed!
The team built a machine learning driven visualization app that matches land conflict events from news articles with mediating government policies. This enables policy makers to make data-driven decisions and resolve land conflicts faster, save resources, and facilitate environmental sustainability efforts.
The AI project was hosted by the World Resources Institute with a focus on India as a country struggling heavily with land disputes.
A detailed whitepaper from the project can be downloaded here.
Why: AI for environmental sustainability
Land degradation affects 3.2 billion people and costs the global economy about 10 percent of the gross product each year. While dozens of countries have committed to restore 350 million hectares of degraded land, land disputes are a major barrier to effective implementation. Without streamlined access to land use rights, landowners are not able to implement sustainable land-use practices. The problem’s scale requires a scalable solution. In India, where 21 million hectares of land have been committed to the restoration, land conflicts affect more than 3 million people each year. Luckily, AI and machine learning offer tremendous potential to not only identify land-use conflicts events but also match suitable policies.
The solutions: Machine Learning and NLP for identifying land use conflicts
This AI challenge resulted in several solutions such as:
- Applying coreference resolution to news text data using SpaCy and Neuralcoref.
- Using ELMo, BERT, and Logistic Regression algorithms to train on labeled and co-referenced text data to predict positive and negative conflict articles.
- Topic modeling to find relevant topics using CorEx with an accuracy score of 93 percent.
- Matching government policies to the conflict news events to understand policy gaps.
- Visualization of conflict events and their connection to policies.
The data: Land use conflict news articles.
The data for this Omdena challenge was scrapped from various news media reports with 65,000 candidate conflict articles. This process involved downloading GDELT data for a given country for an input period of time using Google Bigquery, scrapping full news text for a media article using news-please, and manually labeling one month of news media data as Negative (no conflict news) and Positive (conflict news) with approximately 1,600 articles.
The impact: AI for environmental sustainability.
Land conflicts in India
According to the Environmental Justice Atlas, India has the most number of environmental conflicts, followed by Colombia and Nigeria. For instance, approximately 66% of all civil cases in the Supreme court of India are related to land disputes for more than 2.5 million hectares of land. This affects an estimated 7.7 million Indians threatening investments worth $ 200 billion, according to the June 2019 report by the Centre for Policy Research.
The solutions will help policymakers to make data-driven decisions in a more accurate and efficient way.
We are thanking all community collaborators for the amazing work done! AI For Environmental Sustainability!
This project has been hosted with our friends at
-
WRI is a global research organization that spans more than 60 countries and more than 1,000 experts and staff working closely with leaders to turn big ideas into action to sustain our natural resources—the foundation of economic opportunity and human well-being. Their work focuses on seven critical issues at the intersection of environment and development: climate, energy, food, forests, water, cities, and the ocean.
Kritika Rupauliha
Rosana de Oliveira Gomes
“After all this mind-blowing experience, I feel much closer to the person I wanted to be for a very long time: a technology change maker. “
Colton Magnant
“I have learned so much in several domains including data mining, AI, ML, transfer learning, NLP.”
Anita Okoh
“Working in a collaborative project helped greatly in boosting my data science confidence.”
Xavier Torres Fatsini
“After the Omdena project, I see that career adaptation was a necessary thing in today’s trends.“
Marek Cichy
“Collaboration across continents, time zones, and perspectives, especially on a social science-linked challenge was mind-opening.”
Phenyo Phemelo Moletsane
“Joining Omdena was an important career milestone. A unique way of learning and contributing to social good.”
Neil Sahota
“Collaborative AI enables robust AI solutions through sharing knowledge, perspectives, and promoting diversity and inclusion.“
Animesh Seemendra
“The best part of the challenge was that many in the team were professional data scientists, some of whom with Ph.D. or more than 10 years of experience in the industry.”
Sakthisree Venkatesan
“The community made me feel a sense of freedom and provided a non-judgmental environment where I was enabled to help others.”
