MIT Schwarzman College of Computing’s Post

🌍𝗔𝗜 𝗖𝗮𝗺𝗲𝗿𝗮 𝗳𝗼𝗿 𝗕𝗶𝗼𝗱𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴. We are not alone in tackling this challenge. And that’s truly a good thing! 🙏🏼 The more people interested = the better for the environment Biodiversity is in a catastrophic state. Long reports alone can't solve this. 👉🏼 Only innovative technologies can make a difference. What is an AI-powered camera for biodiversity monitoring? 1️⃣  𝗜𝘁’𝘀 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 + 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 → An AI-enabled camera for biodiversity monitoring combines advanced image recognition + machine learning to automatically identify, classify, and analyze species in real-time. It's not the same like CCTV or a regular camera that simply captures images. 2️⃣  𝗜𝘁’𝘀 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗰𝗹𝗼𝘂𝗱/𝗲𝗱𝗴𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻. → Equipped with AI algorithms, these cameras can differentiate between species, detect subtle patterns, and record environmental data, even in challenging conditions. 3️⃣ 𝗜𝘁 𝗼𝗽𝗲𝗿𝗮𝘁𝗲𝘀 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗶𝗱𝗱𝗹𝗲 𝗼𝗳 𝗺𝗮𝗻𝗴𝗿𝗼𝘃𝗲 / 𝘁𝗿𝗼𝗽𝗶𝗰𝗮𝗹 𝗳𝗼𝗿𝗲𝘀𝘁𝘀 / 𝘁𝗮𝗶𝗴𝗮 / 𝗲𝘁𝗰. This automation allows for continuous monitoring without human intervention. However, there may still be a need to come once a month to wipe off bird droppings and insect traces :). 4️⃣ 𝗧𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝗔𝗜 𝗰𝗮𝗺𝗲𝗿𝗮 𝗶𝘀 𝗶𝗻 𝗵𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗽𝗼𝘂𝗻𝗱𝘀, 𝗻𝗼𝘁 𝘁𝗵𝗼𝘂𝘀𝗮𝗻𝗱𝘀 💰 Nature is non-profit. Developing an affordable hardware solution is a long story and sometimes financially painful. Surprisingly, the biggest challenge isn’t always the cost of components, but... cost of their delivery. However, we still see the opportunity NOT to sell stuff for £5,000 per item. 5️⃣ In the image below, you can see a 𝗯𝗲𝗵𝗶𝗻𝗱-𝘁𝗵𝗲-𝘀𝗰𝗲𝗻𝗲𝘀 𝗹𝗼𝗼𝗸 𝗮𝘁 𝗼𝘂𝗿 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. A few months ago on a cold cloudy day, during one of our experiments in an urban environment I took this screenshot. The camera was literally looking at me! And yes, our AI-powered camera will have a mobile-first dashboard. What do you think of this all this story with AI for biodiversity monitoring?

A fascinating critical assessment of #GeoAI developments in #conservation! “Support for conventional experimentation and on-the-ground practices, which already struggle to attract resources, could be redirected towards financially wealthy institutions which are able to undertake AI work. This could be especially true in conservation research where grounded field-based and participatory studies, which have a role in advancing understanding and local ownership, may become ever more difficult to fund. This could undermine efforts to improve the diversity of voices, knowledge, and approaches in conservation. Hence it is important that funders recognize the importance of supporting a spectrum of conservation research and practice that embraces both conventional and AI approaches. There is a fear that we could see a loss of essential skills in conservation if people in the field pivot towards implementing AI over conventional techniques. Retaining species, ecosystem, and community experts will be integral to creating reliable AI technology. Data is the fuel of AI, and data collected by conservation experts will be essential for producing better models, and this crucial data-collection work must be appropriately recognized. Moreover, information itself, however it is obtained, does not lead to better conservation, and it is important that any recommendations are designed to work in the real world and are not detached from the social and ecological reality on the ground. AI colonialism is a central concern – data potentially extracted from the Global South might be forwarded predominantly to data centers in the Global North for training AI models, followed by AI-driven mandates being issued to the Global South on how land and resources should be managed. This would undermine the efforts of the conservation community to address the colonial legacy of contemporary conservation and recognize the importance of indigenous rights and voices. Furthermore, there is a risk that AI contributes to a militarization of conservation – where computer systems, developed far from the area concerned, identify infractions and trigger enforcement without understanding the local context. Given that local perceived legitimacy is essential to promote compliance with conservation rules [87], this could create or exacerbate conflict. To address digital inequalities and injustices, and to produce less biased, fairer, and more robust information for conservation actions, there is a need to integrate epistemic feedback loops into black box models. This can be achieved by leveraging human-in-the-loop designs as well as political agencies and democratic decision-making [88].”

#Artificial_intelligence is revolutionizing #Ecological_engineering, helping us design #Sustainable solutions to complex environmental challenges. From #Ecosystem restoration to precision agriculture, AI tools provide unprecedented insights. But as with any technology, there are kinks to iron out: ☘️ #Data_Gaps: AI relies on robust datasets, yet ecological data can be fragmented or biased, potentially skewing outcomes. ☘️ Complexity of nature: Ecosystems are dynamic and nonlinear, often defying the predictive models AI creates. ☘️ #Ethical concerns: Automated decisions about resource use or #Biodiversity management raise ethical dilemmas. ☘️ #Tech_Integration: Deploying AI in the field can be challenging due to limited #Infrastructure or resistance to change. The good news? These challenges are opportunities for innovation. By collaborating across disciplines.. #Ecology, #Tech, and #Policy, we can address these limitations and refine AI applications to support a thriving planet! #AI #Ecological_Engineering #Sustainability_Tech #ManoubaSchoolOfEngineering #For_the_Future ☘️

Happy to share the publication of our new research, which I had the privilege to contribute to during my master’s thesis at the Biodiversity and Landscape Lab, Gembloux Agro-Bio Tech ! Submitted in 2021, editorial hurdles prolonged the process, pushing us to adapt and emphasize the originality of our approach in a rapidly evolving field with similar recent publications. ➡ https://lnkd.in/eAQva6Er Context : 🏞 Visitor monitoring in natural areas is crucial for ecosystem management and for assessing the indirect economic benefits these areas provide to local communities. Yet, accurately measuring visitor impact in large, open areas with dispersed access points remains challenging. Our study highlights the potential of camera traps 📸 to monitor both visitor numbers and behavior (while respecting privacy—details in the article) and provide valuable data for sustainable management. A key challenge is the massive volume of data—over 700,000 images collected in a year—which is impractical for manual analysis by natural area managers. To address this, we used a convolutional neural network (CNN, deep learning algorithm) 🤖 to automate the detection and classification of hikers 🚶♂️, dog-walkers🐕🦺 , and cyclists 🚴♀️(but it can be applied to any other moving objects such as wildlife 🐺 or vehicles 🚙). ⭐Key takeaways from the research: - Technical and logistical considerations in the processing chain: Effective camera setup is crucial for automating image classification in outdoor settings using AI. This requires careful attention to camera placement regarding environmental conditions, but also human behavior to ensure accurate results. - Management implications learned from case study across four areas in the Belgian Ardenne: The Hautes Fagnes-Eifel, Haute-Sûre Forêt d’Anlier, Deux Ourthes Natural Parks, and the Great Saint-Hubert Forest. 🌍 This approach offers valuable insights for enhancing visitor management, reducing ecosystem impact, and promoting sustainable tourism, ensuring these natural areas to thrive. 📄 Read the full study here: https://lnkd.in/eAQva6Er #AI #ArtificialIntelligence #MachineLearning #SustainableTourism #EcosystemManagement #VisitorMonitoring #WildlifeProtection #NatureConservation #DeepLearning #CameraTraps #Biodiversity #TechForGood #SustainableDevelopment #BelgianArdenne #TourismManagement #DataScience #ConservationTech #CNN #NaturalParks #SmartTourism

https://lnkd.in/gBHiamSu Artificial intelligence is revolutionizing the battle against deforestation by offering a plethora of tools and capabilities that were previously unimaginable. From aiding in carbon capture calculations to enabling real-time monitoring of logging activities and forest destruction, AI is truly a game-changer in the fight to protect our planet's precious forests. With its ability to process vast amounts of data at lightning speed, AI can analyze massive satellite images with unparalleled efficiency, detecting subtle changes in forest cover that might go unnoticed by the human eye. Furthermore, AI's role in forest conservation extends beyond just monitoring. By utilizing advanced technologies like deep learning, AI can provide accurate tree segmentation and predictive analytics to forecast future deforestation trends. This invaluable insight not only helps in early detection but also in the development of proactive conservation policies and practices. The integration of AI with satellite imaging and drone technology allows for comprehensive and continuous monitoring of forests, enabling governments, NGOs, and environmental organizations to track and combat deforestation on a global scale. As we look towards the future, the potential of AI in combating deforestation is limitless. By combining AI with policy-making and public engagement, we can create a sustainable future for our forests. The advancement of technologies like deep learning and user-friendly interfaces will further enhance AI's monitoring capabilities, ensuring that we can protect our forests for generations to come. Collaborative efforts between stakeholders are essential to leverage the power of AI in the fight against deforestation and to secure a healthier planet for all. No-code Computer Vision for Earth - deepblock.net #remotesensing #earthobservation #computervision #ai #ml #carboncapture #esg #environmentmonitoring #tree #rainforest #satelliteimaging #geoai #geospatial #nocode #deeplearning #machinelearning #aitool #aiplatform

Where AI meets conservation: a step forward for kākā in Wellington 🦜 Exciting mahi is underway thanks to a groundbreaking AI project that’s just received a $1 million boost from the Ministry of Business, Innovation and Employment Smart Ideas fund! This innovative research led by Dr Rachael Shaw and Andrew Lensen at Victoria University of Wellington is changing how we track and protect one of Wellington's most iconic species. With kākā spreading beyond Zealandia Te Māra a Tāne’s fence, keeping track of these birds has become increasingly challenging. AI is stepping in to help, achieving up to 95% accuracy in identifying individual birds. It turns out that kākā have distinct postures that the AI is tracking instead of their beaks! This project demonstrates that artificial intelligence can be used for good, such as conservation and research. The role of technology in conservation is increasing, but the researchers are also learning a connection with mātauranga Māori. Follow the progress of this project and how it impacts the future of kākā conservation right here at Zealandia!

Machine Learning in Environmental Monitoring: Advancing Sustainability and Conservation Machine Learning (ML) is transforming environmental monitoring by enhancing the accuracy of data collection, predicting environmental changes, and supporting conservation efforts. By leveraging ML technologies, researchers and policymakers can make more informed decisions to protect natural resources and promote sustainability. One of the key applications of ML in environmental monitoring is in data analysis and pattern recognition. ML algorithms can process vast amounts of data from sensors, satellites, and other sources to identify trends and anomalies in environmental conditions. This capability allows researchers to monitor changes in air quality, water quality, soil health, and biodiversity with greater precision. For example, ML can detect subtle changes in pollutant levels in the air or water, enabling timely interventions to prevent environmental degradation. ML also plays a crucial role in climate modeling and prediction. By analyzing historical climate data and current weather patterns, ML models can predict future climate scenarios with higher accuracy. These predictions help policymakers develop effective strategies for mitigating the impacts of climate change, such as extreme weather events, rising sea levels, and shifts in agricultural productivity. For instance, ML can forecast the occurrence and intensity of hurricanes, allowing for better preparedness and response. In addition to climate modeling, ML enhances wildlife conservation efforts. By analyzing data from camera traps, drones, and tracking devices, ML models can monitor animal populations, track migration patterns, and detect illegal poaching activities. This real-time monitoring helps conservationists protect endangered species and manage wildlife reserves more effectively. For example, ML algorithms can identify specific animals in camera trap images, providing insights into population dynamics and habitat use. Furthermore, ML supports the management of natural resources by optimizing the use of water, energy, and land. In agriculture, ML models can analyze data on soil moisture, crop health, and weather conditions to recommend precise irrigation and fertilization practices. This precision agriculture approach reduces water and chemical use, promoting more sustainable farming practices. Similarly, ML can optimize the management of forests, fisheries, and other natural resources to ensure their long-term sustainability. #MachineLearning #EnvironmentalMonitoring #ClimateModeling #WildlifeConservation #NaturalResourceManagement #DisasterRiskReduction #EnvironmentalPolicy #CitizenScience #Sustainability #AI #TechInnovation

So many articles and conversations express fear around AI. The response is very similar to the ones received by the invention of the telephone and computers. It’s fear of the unknown and our little emotional brain freaks out. This article is one example of many that illustrate how AI can make life better for civilization… #AI #management #innovation #future #futurism