Swarm UA V autonomous flight for Embodied Long-Horizon (ELH) tasks is crucial for advancing the low-altitude economy. However, existing methods focus only on specific basic tasks due to dataset limitations, failing in real-world deployment for ELH tasks. ELH tasks are not mere concatenations of basic tasks, requiring handling long-term dependencies, maintaining embodied persistent states, and adapting to dynamic goal shifts. This paper presents U2UData+, the first large-scale swarm UA V autonomous flight dataset for ELH tasks and the first scalable swarm UA V data online collection and algorithm closed-loop verification platform. The dataset is captured by 15 UA Vs in autonomous collaborative flights for ELH tasks, comprising 12 scenes, 720 traces, 120 hours, 600 seconds per trajectory, 4.32M LiDAR frames, and 12.96M RGB frames. This dataset also includes brightness, temperature, humidity, smoke, and airflow values covering all flight routes. The platform supports the customization of simulators, UA Vs, sensors, flight algorithms, formation modes, and ELH tasks. Through a visual control window, this platform allows users to collect customized datasets through one-click deployment online and to verify algorithms by closed-loop simulation. U2UData+ also introduces an ELH task for wildlife conservation and provides comprehensive benchmarks with 9 SOT A models.

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real - time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real - time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno - canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel - Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real - world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation.

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes this data with convolutional neural networks (CNNs) and Transformer architectures to monitor species, including those which are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g. poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform's success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints, while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation. Animal ecologists are increasingly limited by constraints in data processing. Here, Tuia and colleagues discuss how collaboration between ecologists and data scientists can harness machine learning to capitalize on the data generated from technological advances and lead to novel modeling approaches.

The global pandemic has given us all an opportunity to pause for thought and take stock of what is and what is not important. More and more businesses are turning to AI to become more sustainable, smarter and to better react to changing market conditions, as well as to ensure health, safety and social impact of our planet. We need a future where you can do the things you love; live the life you deserve and take the time to grow with nature and nurture the things that inspire you to help others. From pandemic prevention and fighting cancer, to fighting hunger, wildlife conservation and boosting accessibility, this article will explore exactly how AI is doing well by doing good. AI use cases can help towards overall adaptation in preventing wildfires, diagnosing deadly diseases, mitigating risks posed in critical areas as well as predictive analysis and monitoring to make our planet more resilient in the near future.

Did you know that scientists have identified only 1.5 million species out of the 10 million estimated on Earth? And many of those species are vulnerable to extinction. Thanks to the efforts of the non-profit organization Wild Me, the gargantuan task of wildlife preservation is getting a much-needed assist from citizen scientists who photograph and video wildlife when traveling the world, plus high-tech solutions such as cloud computing, artificial intelligence, and machine vision. The Amazing Ways Wild Me Uses Artificial Intelligence And Citizen Scientists To Help With ... [ ] Conservation To make the progress on wildlife conservation that's necessary, it's going to take pulling data out of proprietary data sets and joining them into collaborative data sets. This is precisely what Wild Me and its Wildbook platform can do for the effort.

Technology can now help you find the nearest restaurant that has your favorite food and have it delivered to you by speaking to your smart speaker or phone. Did you know that the same technology is being used to help save species from extinction? Our wild elephant conservation team wants to share how AI technology is helping elephant conservation efforts. Artificial intelligence (AI) is the ability of a machine to simulate human intelligence. These machines are programmed to think and act like humans.

The University of Bristol (UoB) has partnered with Bristol Zoological Society (BZS) to develop a trailblazing approach to wildlife conservation, harnessing the power of machine learning and drone technology to transform wildlife conservation around the world. Backed by the Cabot Institute for the Environment, BZS and EPSRC's CASCADE grant, a team of researchers travelled to Cameroon in December last year to test a number of drones, sensor technologies and deployment techniques to monitor the critically endangered Kordofan giraffe populations in Bénoué National Park. "There has been significant and drastic decline recently of larger mammals in the park and it is vital that accurate measurements of populations can be established to guide our conservation actions," said Dr Gráinne McCabe, head of field conservation and science at BZS. "Bénoué National Park is very difficult to patrol on foot and large parts are virtually inaccessible, presenting a huge challenge for wildlife monitoring. What's more, the giraffe are very well camouflaged and often found in small, transient groups," said Dr Caspian Johnson, conservation science lecturer at BZS. Striving to uncover the best method for airborne wildlife monitoring, BZS reached out to Dr Matt Watson from the UoB's School of Earth Sciences, and Dr Tom Richardson from the University's Aerospace Department, as well as a member of the Bristol Robotics Laboratory (BRL). The team forged successful collaborations using drones to monitor and measure volcanic emissions to create a system for wildlife monitoring.

Artificial intelligence is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to old but persistent problems. From a technological point of view, the amount of daily data produced in the digital universe now allows for state-of-the-art approaches, which may lead to innovative solutions in these underserved areas. AI for social good turned into a reality for us at Tryolabs after we collaborated with an NGO to improve upon how African lions are tracked, which helps with species preservation. We will go into more detail on that timely case, especially as wildlife conservation faces the immense challenges posed by devastating megafires threatening the lives of millions of animals in historic ways.

DeepMind's New AI Tracks Serengeti Herds from Images Alone DeepMind, the U.K.-based AI research subsidiary acquired by Alphabet in 2014 for $500 million, today detailed ecological research its science team is conducting to develop AI systems that'll help study the behavior of animal species in Tanzania's Serengeti National Park. They extend the popular BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional transformer layers.