Habitat Labs, a research and innovation division of Habitat Lens Pvt Ltd, is dedicated to harnessing technology for environmental conservation and sustainability. Our mission is to create innovative solutions that promote coexistence between humans and wildlife, protect biodiversity, and advance sustainable practices globally. We envision a future where advanced technology seamlessly integrates with conservation efforts, fostering harmonious interactions between humans and the natural world. Our goals include developing cutting-edge technological solutions tailored to specific environmental challenges, ensuring cost-effectiveness and accessibility, and maintaining scientific rigor through rigorous research and testing. We provide customized, location-specific solutions and forge strong partnerships with government and non-governmental agencies to align with national environmental priorities.
Our product portfolio includes Smart-NLD for non-lethal wildlife deterrence, AI-based image analysis software for ecological monitoring, and Soundscapes for rapid biodiversity assessment, demonstrating our commitment to technical progress and innovation in environmental science and technology.
Our product portfolio includes Smart-NLD for non-lethal wildlife deterrence, AI-based image analysis software for ecological monitoring, and Soundscapes for rapid biodiversity assessment, demonstrating our commitment to technical progress and innovation in environmental science and technology.
PRODUCTS
TRICHO-VISION
Tricho-Vision serves as a pioneering tool that leverages advanced computer vision to identify mammalian species based on microscopic hair characteristics, addressing crucial challenges in wildlife conservation and forensic science. By analysing hair cuticle patterns, medulla structure, and other key features using machine learning models like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), the framework provides accurate identification across taxonomic levels, from species to order. This ability aids law enforcement agencies in curbing wildlife crime, supports researchers in studying biodiversity, and enhances conservation strategies for species at risk. The Tricho-Vision framework demonstrates the power of artificial intelligence in wildlife protection, serving as a vital resource for monitoring biodiversity, investigating wildlife crime scenes, and understanding ecosystem dynamics. Its application ensures that endangered and protected species receive the attention needed for survival and enforcement of international conservation laws.
SMART NLD
The Smart Non-Lethal Deterrent (Smart-NLD) is an AI based innovative animal-repellent fencing system designed to mitigate human-wildlife conflicts by using animal-specific light and sound deterrents. This response-based system recognizes conflict animals and effectively deters them from entering areas of interest, ensuring the safety of both humans and animals. The Smart-NLD addresses issues such as wildlife approaching human settlements, livestock depredation, and crop raiding. Its novel combination of technological features using image recognition and creates an ethical and environmentally conscious solution that reduces habituation by wild herbivores and carnivores. Beyond agricultural and forest settings, the Smart-NLD holds promise for broader applications, including serving as an early warning system in railway zones prone to wildlife collisions, significantly contributing to the safety and well-being of both humans and animals in the country.
OUR WORKS
Individual Identification of Clouded Leopards Using AI-Based Technology
Project Overview: This project aimed to quantify the number of distinct clouded leopards within Buxa Tiger Reserve, West Bengal, India, using strategically placed camera traps. The primary challenge was accurately identifying and differentiating individual leopards based on their unique stripe and marking patterns.
Innovative Technology: To address this challenge, we implemented a robust pattern matching strategy using cutting-edge AI technology, including:
- Super-Point Algorithm: Used for key point detection, this self-supervised algorithm adapts to variations in scale, rotation, and lighting conditions, generating distinctive feature descriptors from leopard images.
- Super-Glue Algorithm: Utilizing graph neural networks and attention mechanisms, this algorithm accurately matched patterns to identify individual leopards, ensuring precise results by focusing solely on unique markings and excluding background interference.
- Segmentation Masks: Generated to isolate leopards from the background, enhancing the accuracy of the pattern matching process.
Implementation and Results: Implemented using Python libraries like OpenCV, PyTorch, and Pillow, our system analyzed 98 images, identifying 66 distinct clouded leopard individuals from 76 usable photos. This demonstrates the potential of AI technology to provide reliable population estimates, crucial for conservation efforts.
Impact: The successful identification and counting of individual clouded leopards advance our understanding of their population dynamics and distribution, contributing valuable data for ecological studies and conservation strategies. This project underscores the power of AI in wildlife conservation, offering a scalable and efficient solution for monitoring endangered species.
Tricho-Vision serves as a pioneering tool that leverages advanced computer vision to identify mammalian species based on microscopic hair characteristics, addressing crucial challenges in wildlife conservation and forensic science. By analysing hair cuticle patterns, medulla structure, and other key features using machine learning models like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), the framework provides accurate identification across taxonomic levels, from species to order. This ability aids law enforcement agencies in curbing wildlife crime, supports researchers in studying biodiversity, and enhances conservation strategies for species at risk. The Tricho-Vision framework demonstrates the power of artificial intelligence in wildlife protection, serving as a vital resource for monitoring biodiversity, investigating wildlife crime scenes, and understanding ecosystem dynamics. Its application ensures that endangered and protected species receive the attention needed for survival and enforcement of international conservation laws.
The Smart Non-Lethal Deterrent (Smart-NLD) is an AI based innovative animal-repellent fencing system designed to mitigate human-wildlife conflicts by using animal-specific light and sound deterrents. This response-based system recognizes conflict animals and effectively deters them from entering areas of interest, ensuring the safety of both humans and animals. The Smart-NLD addresses issues such as wildlife approaching human settlements, livestock depredation, and crop raiding. Its novel combination of technological features using image recognition and creates an ethical and environmentally conscious solution that reduces habituation by wild herbivores and carnivores. Beyond agricultural and forest settings, the Smart-NLD holds promise for broader applications, including serving as an early warning system in railway zones prone to wildlife collisions, significantly contributing to the safety and well-being of both humans and animals in the country.
Individual Identification of Clouded Leopards Using AI-Based Technology
Project Overview: This project aimed to quantify the number of distinct clouded leopards within Buxa Tiger Reserve, West Bengal, India, using strategically placed camera traps. The primary challenge was accurately identifying and differentiating individual leopards based on their unique stripe and marking patterns. Innovative Technology: To address this challenge, we implemented a robust pattern matching strategy using cutting-edge AI technology, including:- Super-Point Algorithm: Used for key point detection, this self-supervised algorithm adapts to variations in scale, rotation, and lighting conditions, generating distinctive feature descriptors from leopard images.
- Super-Glue Algorithm: Utilizing graph neural networks and attention mechanisms, this algorithm accurately matched patterns to identify individual leopards, ensuring precise results by focusing solely on unique markings and excluding background interference.
- Segmentation Masks: Generated to isolate leopards from the background, enhancing the accuracy of the pattern matching process.