Detecting and Monitoring Land Use Transformation in Córdoba, Argentina Using Satellite Imagery

Start Date: January 4, 2025 | a year ago

Forest and cultivated fields Córdoba Argentina. Photo by Ana Calviño, CC BY-SA 4.0, via Wikimedia Commons

Challenge Background

Córdoba, Argentina, is experiencing rapid land-use changes due to urban expansion, agricultural development, deforestation, and other human activities. These transformations impact biodiversity, water resources, and the regional climate, posing challenges for sustainable development and environmental conservation. Despite the critical need to monitor these changes, there is a lack of accessible tools that allow authorities, organizations, and citizens to effectively detect and analyze land-use transformations over time. This project aims to address this gap by developing a change detection tool using satellite imagery from two different time periods (t1 and t2).

The Problem

There is an urgent need to monitor land-use changes in Córdoba to support sustainable development and environmental protection. The lack of effective tools for detecting these changes hinders the ability of authorities and organizations to implement timely interventions and policies. By developing a model that detects changes between two satellite images taken at different times, we can provide valuable insights into urban growth, deforestation, and other land transformations. This will enable better decision-making and promote transparency in land management practices, ultimately benefiting the local community.

Goal of the Project

Develop a robust change detection model that analyzes satellite images of Córdoba from two different time points (t1 and t2).
Identify and map areas undergoing significant land-use changes, such as urban expansion or deforestation.
Provide an accessible platform for authorities, organizations, and citizens to monitor and analyze these changes.
Enhance transparency and support decision-making processes related to land management and environmental conservation.

Project Timeline

Week 1: Data Collection

Identify Data Sources: Research and select free or low-cost satellite imagery providers (e.g., Sentinel-2, Landsat 8).
Download Images: Acquire satellite images of Córdoba at two different times (t1 and t2).
Gather Ancillary Data: Collect additional data such as land cover maps and urban planning documents for context.

Week 2: Data Preprocessing

Image Correction: Perform atmospheric and geometric corrections to standardize the images.
Image Registration: Align images from t1 and t2 to ensure accurate pixel-to-pixel comparison.
Band Selection: Choose relevant spectral bands that are sensitive to the types of changes being analyzed.

Week 3: Exploratory Data Analysis

Data Understanding: Analyze the characteristics of the images to identify patterns and anomalies.
Calculate Indices: Compute indices like NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) to highlight vegetation and urban areas.
Preliminary Mapping: Create initial maps to visualize potential areas of change.

Week 4: Change Detection Modeling

Implement Techniques: Apply pixel-based and object-based change detection methods to identify changes.
Algorithm Development: Use machine learning algorithms to classify types of land-use changes.
Model Validation: Validate the model's accuracy using ground truth data or high-resolution imagery.

Week 5: Analysis and Interpretation

Temporal Analysis: Study the nature and extent of changes over time between t1 and t2.
Spatial Analysis: Identify hotspots and patterns of significant land-use change.
Reporting: Compile findings into reports to communicate insights effectively.

Week 6: API Development

Design API Architecture: Outline the structure, endpoints, and functionalities of the API.
Develop API: Build the API to allow external access to the change detection model and results.
Documentation: Write clear and comprehensive documentation for API users.

Week 7: Front-end Development

UI/UX Design: Create wireframes and design an intuitive user interface.
API Integration: Connect the front-end application with the back-end API.
Interactive Features: Implement features like interactive maps, layer controls, and data visualization tools.

Week 8: Deployment

Hosting Setup: Choose a cloud platform (e.g., AWS, Azure, Heroku) for deployment.
Application Deployment: Launch both back-end and front-end services online.
Testing and Quality Assurance: Conduct thorough testing to identify and fix bugs, and ensure performance and security standards are met.

What you'll learn

Participants will gain:

Data Collection Skills: Acquiring and preprocessing satellite imagery from multiple sources.
Image Processing Expertise: Applying remote sensing techniques to detect changes between images.
GIS Proficiency: Using Geographic Information Systems for spatial analysis and mapping.
Machine Learning Experience: Implementing algorithms to classify and predict land-use changes.
API Development Skills: Creating APIs to provide access to the change detection tool.
Front-end Development Knowledge: Building user-friendly interfaces for data visualization.
Deployment Experience: Hosting applications online for public or institutional access.
Environmental Insight: Understanding the impact of land-use changes on local communities and ecosystems.

First Omdena Local Chapter Project?

Beginner-friendly, but also welcomes experts

Education-focused

Duration: 4 to 8 weeks

Open-source

Your Benefits

Address a significant real-world problem with your skills

Build your project portfolio

Access paid projects (as an Omdena Top Talent)

Get hired at top organizations

Requirements

Good English

Suitable for AI/ Data Science beginners but also more senior collaborators

Learning mindset

Application Form

Application Closed.