This building block is to explain how I developped an App that allow fishers to contribute to marine science knowledge in Africa. 

Initially we gave fishers a pre-printed form to report opportunistic sightings they encountered. However, the form was getting lost most of the time. 

We decided to move to a digital solution. The existing App by then required internet to work and was just too complicated for fishers. So we thought we shoud develop an App that will be more userfriendly for fishers. 

We wrote the  algorithm (workflow) of the App and then contracted an Indian development company to write the code. 

Later we had to bring the development of SIREN back to Cameroon to reduce the cost of developement. 

We work with volunteer around the world that will continuously support with the development of the SIREN

This building block emphasizes the importance of training students and local actors in advanced technologies for conservation purposes. In Bio-Scanner, students from the Universidad Politécnica de Yucatán  are trained in using AI algorithms, camera-trap data processing, and decision-support tools, fostering a new generation of professionals equipped to address biodiversity challenges.

This building block is centered on the use of Convolutional Neural Networks (CNNs), including Siamese and Autoencoder architectures, to detect and identify individual jaguars based on unique features such as rosette patterns and morphology. These algorithms process camera-trap data efficiently, reducing the time required for analysis and providing critical insights for decision-making in conservation.

This building block provides the essential spatial intelligence for PyroSense, enabling a dynamic understanding of the geographical landscape. Its core purpose is to identify fire risk areas, pinpoint incident locations, and visualize resource deployment. This is crucial for strategic decision-making, allowing proactive resource allocation, and response planning. 

PyroSense utilizes a robust Geographic Information System (GIS) to power this function. The GIS integrates various spatial data layers, including topography, vegetation, infrastructure, etc. Initially, baseline risk maps are created by analyzing factors, guiding the placement of sensors and cameras.

Upon detection of a potential fire by environmental sensors or AI, the system immediately feeds the precise coordinates into the GIS. This real-time location data, combined with meteorological data (local and satellite), enables dynamic risk assessments. The GIS also serves as a central operational dashboard, visualizing the real-time positions of all deployed assets, including drones and first responder teams. This facilitates optimal resource allocation and coordination. This critical information is then communicated via a web application to stakeholders, providing clear visual situational awareness and supporting informed decision-making. 

This is the comprehensive intake mechanism for all information vital to PyroSense's platform. Its purpose is to gather real-time data, from multiple origins, ensuring the system has the input needed for accurate analysis and effective decision-making. 

PyroSense integrates an agnostic and highly compatible array of data:

1. Environmental IoT Sensors are strategically deployed, and continuously collect real-time CO2, temp. and humidity data. They are agnostic in type and protocol, compatible with MQTT, LoRa, Sigfox, and NBIoT, ensuring broad integration. For efficiency, they feature long-lasting batteries (up to 10 years), minimising maintenance.  

2. Fixed cameras and drones capture high-resolution images and live video. Integrated Vision AI processes this visual data in real-time to detect anomalies like smoke or fire. 

3. PyroSense gathers data from local weather stations and satellites. Combining granular local data with broad satellite coverage provides a comprehensive understanding of current weather.

4. GIS provides foundational spatial information, including maps of terrain, vegetation,  infrastructure, etc. 

5. Firemen Wearables monitor real-time biometrics. AI enhances data for risk pattern recognition, of fatigue or heat stress. Real-time alerts are sent to nearby teams or control centers, enabling proactive intervention.

A process designed to estimate the chronic impacts of mining activities on the landscape—such as habitat loss, fragmentation, and degradation. This analysis generates a gradient of exposure for biodiversity and speleological heritage, indicating varying levels of environmental damage severity. The mining impact exposure map provides a spatial representation of the risks to which conservation targets are subjected, allowing for a detailed assessment of biodiversity vulnerability. Identifying the areas most intensely affected by mining enables more strategic and informed planning efforts to minimize biodiversity loss.

The process involves coordination with sectoral bodies, the systematization of environmental data, and the validation of results through expert consultation. The methodologies employed are scientifically validated, widely accepted by the academic community, and designed to be replicable across different regions and landscape scales.