Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years

The Gnu Frontier: deploying machine learning and open-source electronics for the study of ungulate movement in the Anthropocene

The Anthropocene epoch has ushered in unprecedented and irreversible changes in many biomes, resulting in the disruption of ecological functions and processes. These changes are largely driven by the increased human footprint on a planetary scale and global warming. Consequently, various impacts have been documented, including the extinction of flora and fauna, modification of ecosystems into more homogeneous covers (e.g., farmlands), increase in human-dominated landscapes, disruptions of animal migrations, species range shifts, invasions leading to the extermination of native species, and encroachment of protected areas. These widespread ecosystem changes have become a primary concern for researchers and policymakers who must maintain a delicate balance between the persistence of species and their habitats and the promotion of sustainable development. Furthermore, the rate at which these changes are occurring outpaces the evolutionary response of many species. Consequently, gaining insight into how species respond to various ecological disruptions, both within and outside protected areas, is imperative. However, a thorough understanding of animal behaviour and their responses to rapid ecosystem changes remains challenging due to the lack of robust tools for collecting fine-grained data. To address this methodological gap, I first use camera trap data to demonstrate how migratory species in the Serengeti ecosystem are spatially distributed in relation to human activities occurring in the immediate landscapes adjoining protected areas. The results reveal that the species tend to avoid areas transitioning into human-dominated landscapes as opposed to those bordering buffer zones. The results hold significant conservation value and illuminate population-level responses to anthropogenic disturbances. However, camera trap data does not provide individual-level behavioural insights. Consequently, it remains unclear which additional factors and social cues animals may be observing when traversing across habitats with varying threat levels and how these factors influence their behaviour. Camera traps and telemetry tools such as GPS alone cannot provide data required to answer such questions; therefore, a different set of tools is necessary. Leveraging the capabilities of open-source electronics, I present a low-cost system for automated and repeated observation of collared animals. This system consists of a GPS collar, a long range network (LoRa) radio transmitter, and a commercially available low-flying unmanned aerial vehicle (UAV), taking advantage of its built-in capacity to track a stream of GPS points. The system was tested on a small group of ponies and demonstrated its efficacy and performance by collecting data on focal individual as well as information about its nearest neighbours. Furthermore, automated tracking system collects data in bursts of approximately 20 minutes, aligning with the flight time capacity of a fully charged battery. As such, obtaining behavioural data for longer periods is difficult which necessitates a different approach. Given the rapid ecological changes, it is crucial to understand animal behaviour and its perception of the immediate surroundings. For instance, where do animals spend more time being vigilant as opposed to engaging in other restorative activities like resting? Such areas could be regarded as risky from an animal’s perspective. In this study, I developed a near real-time animal behaviour classifier using low-cost open-source electronics, a low-power long-range wide-area network (LoRaWAN) for connectivity, and edge machine learning. The custom-designed animal tracking system records behavioural data, preprocesses it, and classifies it into four classes: grazing, lying, standing, and walking. The predicted behaviour classes are transmitted to the end-user via servers in near real-time. The tracking tool was tested on Serengeti wildebeest and demonstrated its performance by sending both behavioural classes alongside positional data of the collared animal. In this study, I have demonstrated the utility of existing remote tracking tools as well as their limitations in addressing evolving ecological questions in relation to animal behaviour and response to ecosystem perturbations. The methodological approaches presented here have the potential to greatly enhance our understanding of animal ecology. Importantly, the application of novel technologies will empower scientists to enhance existing tools, generate complementary data streams, improve data resolution and quantity, and enrich their overall capabilities to study complex questions. For instance, it improves our ability to collect behavioural and positional data, monitor focal individuals, and track nearest neighbours, and potentially opens up other avenues for scientific applications. The application of open-source electronics creates an opportunity for other researchers to customise the tools as an alternative to commercial devices to address specific questions and potentially result in other valuable innovations

Deep Learning Based Sound Event Detection and Classification

Hearing sense has an important role in our daily lives. During the recent years, there has been many studies to transfer this capability to the computers. In this dissertation, we design and implement deep learning based algorithms to improve the ability of the computers in recognizing the different sound events. In the first topic, we investigate sound event detection, which identifies the time boundaries of the sound events in addition to the type of the events. For sound event detection, we propose a new method, AudioMask, to benefit from the object-detection techniques in computer vision. In this method, we convert the question of identifying time boundaries for sound events, into the problem of identifying objects in images by treating the spectrograms of the sound as images. AudioMask first applies Mask R-CNN, an algorithm for detecting objects in images, to the log-scaled mel-spectrograms of the sound files. Then we use a frame-based sound event classifier trained independently from Mask R-CNN, to analyze each individual frame in the candidate segments. Our experiments show that, this approach has promising results and can successfully identify the exact time boundaries of the sound events. The code for this study is available at https://github.com/alireza-nasiri/AudioMask. In the second topic, we present SoundCLR, a supervised contrastive learning based method for effective environmental sound classification with state-of-the-art performance, which works by learning representations that disentangle the samples of each class from those of other classes. We also exploit transfer learning and strong data augmentation to improve the results. Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99.75%, 93.4%, and 86.49% respectively. The ensemble version of our models also outperforms other top ensemble methods. Finally, we analyze the acoustic emissions that are generated during the degradation process of SiC composites. The aim here is to identify the state of the degradation in the material, by classifying its emitted acoustic signals. As our baseline, we use random forest method on expert-defined features. Also we propose a deep neural network of convolutional layers to identify the patterns in the raw sound signals. Our experiments show that both of our methods are reliably capable of identifying the degradation state of the composite, and in average, the convolutional model significantly outperforms the random forest technique

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Counter Unmanned Aircraft Systems Technologies and Operations

As the quarter-century mark in the 21st Century nears, new aviation-related equipment has come to the forefront, both to help us and to haunt us. (Coutu, 2020) This is particularly the case with unmanned aerial vehicles (UAVs). These vehicles have grown in popularity and accessible to everyone. Of different shapes and sizes, they are widely available for purchase at relatively low prices. They have moved from the backyard recreation status to important tools for the military, intelligence agencies, and corporate organizations. New practical applications such as military equipment and weaponry are announced on a regular basis – globally. (Coutu, 2020) Every country seems to be announcing steps forward in this bludgeoning field. In our successful 2nd edition of Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets (Nichols, et al., 2019), the authors addressed three factors influencing UAS phenomena. First, unmanned aircraft technology has seen an economic explosion in production, sales, testing, specialized designs, and friendly / hostile usages of deployed UAS / UAVs / Drones. There is a huge global growing market and entrepreneurs know it. Second, hostile use of UAS is on the forefront of DoD defense and offensive planners. They are especially concerned with SWARM behavior. Movies like “Angel has Fallen,” where drones in a SWARM use facial recognition technology to kill USSS agents protecting POTUS, have built the lore of UAS and brought the problem forefront to DHS. Third, UAS technology was exploding. UAS and Counter- UAS developments in navigation, weapons, surveillance, data transfer, fuel cells, stealth, weight distribution, tactics, GPS / GNSS elements, SCADA protections, privacy invasions, terrorist uses, specialized software, and security protocols has exploded. (Nichols, et al., 2019) Our team has followed / tracked joint ventures between military and corporate entities and specialized labs to build UAS countermeasures. As authors, we felt compelled to address at least the edge of some of the new C-UAS developments. It was clear that we would be lucky if we could cover a few of – the more interesting and priority technology updates – all in the UNCLASSIFIED and OPEN sphere. Counter Unmanned Aircraft Systems: Technologies and Operations is the companion textbook to our 2nd edition. The civilian market is interesting and entrepreneurial, but the military and intelligence markets are of concern because the US does NOT lead the pack in C-UAS technologies. China does. China continues to execute its UAS proliferation along the New Silk Road Sea / Land routes (NSRL). It has maintained a 7% growth in military spending each year to support its buildup. (Nichols, et al., 2019) [Chapter 21]. They continue to innovate and have recently improved a solution for UAS flight endurance issues with the development of advanced hydrogen fuel cell. (Nichols, et al., 2019) Reed and Trubetskoy presented a terrifying map of countries in the Middle East with armed drones and their manufacturing origin. Guess who? China. (A.B. Tabriski & Justin, 2018, December) Our C-UAS textbook has as its primary mission to educate and train resources who will enter the UAS / C-UAS field and trust it will act as a call to arms for military and DHS planners.https://newprairiepress.org/ebooks/1031/thumbnail.jp