One of the impacts of climate change is the difficulty of tree regrowth after wildfires over areas that traditionally were covered by certain tree species. Here a deep learning model is customized to classify land covers from four-band aerial imagery before and after wildfires to study the prolonged consequences of wildfires on tree species. The tree species labels are generated from manually delineated maps for five land cover classes: Conifer, Hardwood, Shrub, ReforestedTree and Barren land. With an accuracy of $92\%$ on the test split, the model is applied to three wildfires on data from 2009 to 2018. The model accurately delineates areas damaged by wildfires, changes in tree species and rebound of burned areas. The result shows clear evidence of wildfires impacting the local ecosystem and the outlined approach can help monitor reforested areas, observe changes in forest composition and track wildfire impact on tree species.
A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification
Department of Electrical and Computer Engineering, University of Illinois Chicago（伊利诺伊大学芝加哥分校电子与计算机工程系）；Informatics Institute, Istanbul Technical University（伊斯坦布尔技术大学信息学院）；USDA Forest Service Pacific Wildland Fire Sciences Laboratory（美国农业部森林服务局太平洋野火科学实验室）；Department of Computer Engineering, Bilkent University（比尔肯特大学计算机工程系）
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Spatial Informatics Group, LLC.（空间信息集团有限责任公司）；ADNET systems, INC.（ADNET系统公司）；NASA Langley Research Center（美国国家航空航天局兰利研究中心）；University at Albany（奥尔巴尼大学）
Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring
Renmin University of China（中国人民大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；International Research Institute of Disaster Science (IRIDeS), Tohoku University（东北大学灾害科学国际研究所）
Annual Earth-observation embeddings encode wildfire disturbance and support simplified burned area mapping
Conservation Research Institute, University of Cambridge（剑桥大学保护研究所）；Department of Plant Sciences, University of Cambridge（剑桥大学植物科学系）；dClimate Labs（dClimate实验室）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）；Department of Earth System Science, Stanford University（斯坦福大学地球系统科学系）
Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification
Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Michael Stifel Center Jena for Data-Driven and Simulation Science（耶拿迈克尔·施蒂费尔数据驱动与模拟科学中心）
WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring
Fondazione Bruno Kessler（布雷诺·科塞勒基金会）；University of Muenster（明斯特大学）；Max Planck Institute of Animal Behaviour（动物行为马克斯·普朗克研究所）；University of Konstanz（康斯坦茨大学）
Universidad de Antioquia UdeA（安蒂奥基亚大学（UdeA））；Instituto Tecnológico Metropolitano ITM（大都会理工学院（ITM））
Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat. However camera trapping framework produces a high volume of data (in the order on thousands or millions of images), which must be analyzed by a human expert. In this work, a method for animal species identification in the wild using very deep convolutional neural networks is presented. Multiple versions of the Snapshot Serengeti dataset were used in order to probe the ability of the method to cope with differ…
University of Washington（华盛顿大学）；National Oceanic and Atmospheric Administration（美国国家海洋和大气管理局）；Pacific States Marine Fisheries Commission（太平洋各州海洋渔业委员会）
The goal of electronic monitoring (EM) of longline fishing is to monitor the fish catching activities on fishing vessels, either for the regulatory compliance or catch counting. Hierarchical classification based on videos allows for inexpensive and efficient fish species identification of catches from longline fishing, where fishes are under severe deformation and self-occlusion during the catching process. More importantly, the flexibility of hierarchical classification mitigates the laborious efforts of human reviews by providing confidence scores in different hierarchical levels. Some relat…
Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation
Mathematical Modeling Laboratory, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University（九州大学生物资源与生物环境科学研究生院数学建模实验室）；International Cooperation Center, Thai Nguyen University（Thai Nguyen大学国际合作中心）；Centrer of Crop Research for Adaptation to Climate Change, Thai Nguyen University of Agriculture and Forestry（Thai Nguyen农业与林业大学适应气候变化作物研究中心）
kabr-tools: Automated Framework for Multi-Species Behavioral Monitoring
The Ohio State University（俄亥俄州立大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Princeton University（普林斯顿大学）；University of Miami（迈阿密大学）；Mpala Research Centre（Mpala研究机构）
IIIT-Delhi（德里英迪拉·甘地信息技术学院）；Wildlife Institute of India（印度野生动物研究所）
Despite loss of natural habitat due to development and urbanization, certain species like the Rhesus macaque have adapted well to the urban environment. With abundant food and no predators, macaque populations have increased substantially in urban areas, leading to frequent conflicts with humans. Overpopulated areas often witness macaques raiding crops, feeding on bird and snake eggs as well as destruction of nests, thus adversely affecting other species in the ecosystem. In order to mitigate these adverse effects, sterilization has emerged as a humane and effective way of population control o…
Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
McGill University（麦吉尔大学）；Mila - Quebec Artificial Intelligence Institute（魁北克人工智能研究所）；University of Copenhagen（哥本哈根大学）；Agriculture and Agri-food Canada（加拿大农业与农业食品部）；Smithsonian Tropical Research Institute（史密松尼热带研究所）；Biology Center, Czech Academy of Sciences（捷克科学院生物学中心）；Maestria de Entomologia, University of Panama（巴拿马大学昆虫学系）；Massachusetts Institute of Technology（麻省理工学院）；Montréal Insectarium（蒙特利尔昆虫馆）
Distant Object Localisation from Noisy Image Segmentation Sequences
Research Council of Finland（芬兰研究理事会）；RCF Flagship Forest–Human–Machine Interplay—Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE)（RCF旗舰森林-人类-机器交互——构建韧性，重新定义价值网络和赋能有意义体验（UNITE））
University of Cambridge（剑桥大学）；Royal Society for Protection of Birds（英国皇家鸟类保护协会）；Jambi University（占碑大学）；PT Restorasi Ekosistem Indonesia（印度尼西亚生态系统恢复公司）
Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crow…
Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries
DTU Aqua - National Institute of Aquatic Resources, Technical University of Denmark（丹麦技术大学国家水体资源研究所）；Visual Analysis and Perception Laboratory, Aalborg University（奥胡斯大学视觉分析与感知实验室）
Predicting butterfly species presence from satellite imagery using soft contrastive regularisation
The Alan Turing Institute, the UK（英国阿尔安·图灵研究所）；Wageningen University & Research, the NL（瓦赫宁根大学与研究中心）；University College London, the UK（伦敦大学学院）；UK Centre for Ecology & Hydrology, the UK（英国生态与水文研究中心）
Neural Tree Reconstruction for the Open Forest Observatory
Berkeley AI Research, University of California, Berkeley（加州大学伯克利分校伯克利人工智能研究）；Department of Plant Sciences, University of California, Davis（加州大学戴维斯分校植物科学系）
From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations
Leibniz Centre for Agricultural Landscape Research (ZALF)（莱比锡农业景观研究中心（ZALF））；Brandenburg University of Technology Cottbus–Senftenberg（勃兰登堡技术大学科特博斯-森芬根堡）
Institut Agro（法国高等农业教育研究院）；INRAE（法国国家农业、食品与环境研究院）；OFB（法国生物多样性办公室）；Univ. Pau & Pays Adour/E2S UPPA（波城与阿杜尔大学/E2S UPPA）；LETG-Rennes（LETG-雷恩）
Underwater acoustic cameras are high potential devices for many applications in ecology, notably for fisheries management and monitoring. However how to extract such data into high value information without a time-consuming entire dataset reading by an operator is still a challenge. Moreover the analysis of acoustic imaging, due to its low signal-to-noise ratio, is a perfect training ground for experimenting with new approaches, especially concerning Deep Learning techniques. We present hereby a novel approach that takes advantage of both CNN (Convolutional Neural Network) and classical CV (Co…
The University of Osaka（大阪大学）；Kyoto University（京都大学）；Tokyo Institute of Technology（东京技术大学）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
Bialystok University of Technology（比亚韦斯托克理工大学）；Mammal Research Institute, Polish Academy of Sciences（波兰科学院哺乳动物研究所）
Camera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Bialowieza Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open source application for managin…
SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping
The University of Sydney（悉尼大学）；The University of New England（新英格兰大学）；Australian Research Council (ARC) Training Centre for CubeSats, UAVs & Their Applications (CUAVA)（澳大利亚研究理事会立方卫星、无人机及其应用培训中心）
Universidade da Beira Interior（贝拉 Interior 大学）；Zirak srl（齐拉克有限责任公司）；ECOATIVO - Associação de Proteção e Conservação da Natureza（ECOATIVO 自然保护与养护协会）
Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. Ever-growing reports of invasive species have affected the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can negativ…
School of Science and Technology, University of New England（新英格兰大学科学与技术学院）；NSW Department of Primary Industries（新南威尔士州初级产业部）；School of Environmental and Rural Science, University of New England（新英格兰大学环境与农村科学学院）
Deep learning (DL) algorithms are the state of the art in automated classification of wildlife camera trap images. The challenge is that the ecologist cannot know in advance how many images per species they need to collect for model training in order to achieve their desired classification accuracy. In fact there is limited empirical evidence in the context of camera trapping to demonstrate that increasing sample size will lead to improved accuracy. In this study we explore in depth the issues of deep learning model performance for progressively increasing per class (species) sample sizes. We …
