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.

Leveraging diverse expertise from various backgrounds, such as fisheries, technology, and governance, to contribute their expertise and experience. Pooling resources from different stakeholders community, government, technology and knowledge partners including INCOIS and Qualcomm to support the development, implementation, and scaling up of the FFMA. Ensuring the FFMA meets the needs of fishers and other stakeholders, increasing its adoption and impact. All these building a strong foundation for the FFMA's long-term sustainability through shared ownership and commitment.

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.

It is the analysis that produces a map with the gradient of vulnerability to the potential impacts of mining tailings dam collapses for environmental risk management. It is the product of cross-referencing information on the impact of potential environmental degradation resulting from the collapse of mining dams and the sensitivity of biodiversity.

Process that defines the most suitable areas for offsetting environmental impacts based on analyses of the similarity of the composition of biodiversity and geodiversity sensitive to mining. This map assumes that the best place to invest efforts to offset the impacts of a mining activity will be those that share the largest number of conservation targets affected by the project. To this end, a spatially explicit hierarchical cluster analysis was performed, which indicates a gradient of similarity between impacted and protected areas, grouped into groups and clusters for offsetting.

The Compatibility Map between Biodiversity and Speleological Heritage Conservation and Mining Activities is represented as a bivariate map, resulting from the spatial overlay of two key components: the Biodiversity Sensitivity Map and the Mining Impact Exposure Map. This integrated approach allows for the identification of areas where conservation priorities and mining pressures intersect, providing a spatial framework to support more informed land-use planning.

In this context, the higher the compatibility of a given area, the lower the associated environmental cost. Such areas are likely to involve less complex environmental licensing processes and require fewer efforts to mitigate biodiversity loss. Conversely, areas of low compatibility indicate a greater potential for conflict between conservation and mining activities.

Impact reduction is primarily achieved by prioritizing the avoidance of low-compatibility zones. Where avoidance is not feasible, specific mitigation and/or compensation measures—tailored to the conservation targets present—must be adopted to ensure the persistence of biodiversity within impacted areas.

This approach demonstrates that it is possible to reconcile biodiversity and geodiversity conservation with mineral extraction through science-based, spatially explicit planning tools that support sustainable development.

The Biodiversity Sensitivity Map provides a spatial representation of the varying degrees of vulnerability of conservation targets to mining-related impacts. It integrates biological and ecological characteristics of species and ecosystems, along with the influence of anthropogenic pressures, to create a comprehensive sensitivity gradient—referred to as the Biodiversity Sensitivity Index.

This index ranks the entire study area into four sensitivity classes, ranging from “Extremely Sensitive Areas” to “Less Concerning Areas”, with each category representing approximately 25% of the total area. The classification follows principles of systematic conservation planning, incorporating spatial representations of both the distribution and sensitivity of each conservation target.

Certain species, habitats, or ecosystem services are more vulnerable due to intrinsic biological or ecological traits, or due to their geographic location. Moreover, the model considers landscape-level attributes—such as environmental conditions that either support or hinder biodiversity persistence—that are not directly tied to the mining threat but are critical for understanding overall ecological resilience.

Importantly, only targets that are likely to become even more vulnerable in the absence of preventive or mitigation measures were included in the mapping, ensuring that the tool supports strategic planning and prioritization for conservation in the context of mineral exploration.

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.

Corruption in the forestry sector continues to undermine the rights & livelihoods of local & Indigenous communities. By institutionalising the use of ForestLink, we empower local communities beyond enforcement - the system has proven critical in tackling this corruption, enabling communities to document land rights violations & illegal activities, defend their territories & secure access to justice, whilst securing sustainable economic opportunities linked to forest resources.  

 Crucially, ForestLink supports sustainable economic activities & lays the groundwork for payment for environmental services by reinforcing community autonomy & stewardship of natural resources. Through partnerships with local organisations skilled in legal advocacy & sustainable enterprise, communities are supported to develop livelihoods aligned with forest protection. Key enabling factors include understanding current economic practices, ensuring financial support for legal actions & engaging in parallel advocacy to secure land rights.  

By actively managing and defending their lands, communities reinforce their autonomy & contribute to long-term, locally driven development. The data collected through the tool also plays a crucial role in supporting access to justice - providing evidence for legal & non-legal actions when communities face human rights abuses or environmental crimes.