Tree Counting by Bridging 3D Point Clouds with Imagery
Lei Li, Tianfang Zhang, Zhongyu Jiang, Cheng-Yen Yang, Jenq-Neng Hwang, Stefan Oehmcke, Dimitri Pierre Johannes Gominski, Fabian Gieseke, Christian Igel
Accurate and consistent methods for counting trees based on remote sensing data are needed to support sustainable forest management, assess climate change mitigation strategies, and build trust in tree carbon credits. Two-dimensional remote sensing imagery primarily shows overstory canopy, and it does not facilitate easy differentiation of individual trees in areas with a dense canopy and does not allow for easy separation of trees when the canopy is dense. We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees. We compare a deep learning approach to counting trees in forests using 3D airborne LiDAR data and 2D imagery. The approach is compared with state-of-the-art algorithms, like operating on 3D point cloud and 2D imagery. We empirically evaluate the different methods on the NeonTreeCount data set, which we use to define a tree-counting benchmark. The experiments show that FuseCountNet yields more accurate tree counts.
Element AI；McGill University（麦吉尔大学）；Oxford / FDL（牛津大学 / FDL）；Global Witness（全球见证）
Cattle farming is responsible for 8.8\% of greenhouse gas emissions worldwide. In addition to the methane emitted due to their digestive process, the growing need for grazing areas is an important driver of deforestation. While some regulations are in place for preserving the Amazon against deforestation, these are being flouted in various ways, hence the need to scale and automate the monitoring of cattle ranching activities. Through a partnership with \textit{Global Witness}, we explore the feasibility of tracking and counting cattle at the continental scale from satellite imagery. With a li…
In this paper, we attempt to address the challenging problem of counting built-structures in the satellite imagery. Building density is a more accurate estimate of the population density, urban area expansion and its impact on the environment, than the built-up area segmentation. However, building shape variances, overlapping boundaries, and variant densities make this a complex task. To tackle this difficult problem, we propose a deep learning based regression technique for counting built-structures in satellite imagery. Our proposed framework intelligently combines features from different re…
Counting Trees from Satellite Imagery with Noisy Supervision
University of Copenhagen（哥本哈根大学）；University of Rwanda（卢旺达大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Stony Brook University（石溪大学）；LIGM, CNRS, Univ Gustave Eiffel, ENPC, IPP（LIGM，CNRS，古斯塔夫·埃菲尔大学，ENPC，IPP）
Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery
Key Laboratory of Intelligent Computing & Signal Processing (Anhui University), Ministry of Education（智能计算与信号处理重点实验室（安徽大学））；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Artificial Intelligence, Anhui University（人工智能学院）；th Research Institute, China Electronics Technology Group Corporation（中国电子科技集团第三十八研究所）；School of Computer Science and Technology, Anhui University（计算机科学与技术学院）
A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery
School of Geospatial Engineering and Science, Sun Yat-Sen University（中山大学地理空间工程与科学学院）；Faculty of Geo-Information Science and Earth Observation, University of Twente（特温特大学地球信息科学与对地观测学院）；Boma and Badingilo National Parks, African Parks（博马和巴丁吉洛国家公园，非洲公园组织）；Department of Ecology and Evolutionary Biology, Princeton University（普林斯顿大学生态与进化生物学系）；Lolldaiga Hills Research Programme（洛尔迪加山研究项目）；School of Biological, Earth & Environmental Sciences, Environmental Research Institute, University College Cork（科克大学环境研究所生物、地球与环境科学学院）
Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers. In this work, we present a deep learning method for simultaneous segmentation and counting of cranberries to aid in yield estimation and sun exposure predictions. Notably, supervision is done using low cost center point annotations. The approach, named Triple-S Network, incorporates a three-part loss with shape priors to promote better fitting to objects of known shape typical in agricultural scenes. Our results improve overall segmentation performance by more than 6.…
Remote Sensing Retrieval-Augmented Generation: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model
School of Cyber Science and Technology, University of Science and Technology of China（信息科学技术学院，中国科学技术大学）；China Academy of Electronics and Information Technology（电子信息技术研究院）
In spite of the many advantages of aerial imagery for crowd monitoring and management at mass events, datasets of aerial images of crowds are still lacking in the field. As a remedy, in this work we introduce a novel crowd dataset, the DLR Aerial Crowd Dataset (DLR-ACD), which is composed of 33 large aerial images acquired from 16 flight campaigns over mass events with 226,291 persons annotated. To the best of our knowledge, DLR-ACD is the first aerial crowd dataset and will be released publicly. To tackle the problem of accurate crowd counting and density map estimation in aerial images of cr…
SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
Universiti Malaya（马来大学）；Kunming University of Science and Technology（昆明理工大学）；Yunnan Normal University（云南师范大学）；Guangming Laboratory（光明实验室）；University of Central Florida（佛罗里达大学）
Computer Vision Center（计算机视觉中心）；MICC, University of Florence（佛罗伦萨大学MICC）
We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing datasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and query-by-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabele…
Weakly supervised framework for wildlife detection and counting in challenging Arctic environments: a case study on caribou (Rangifer tarandus)
Department of Applied Geomatics, Université de Sherbrooke（应用测绘系，谢伯罗克大学）；Quebec Centre for Biodiversity Science (QCBS), Stewart Biology, McGill University（魁北克生物多样性科学中心（QCBS）、斯图尔特生物学、麦吉尔大学）
We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure and context of the scene. In contrast to DR, which places objects and distractors randomly according to a uniform probability distribution, SDR places objects and distractors randomly according to probability distributions that arise from the specific problem at hand. In this manner, SDR-generated imagery enables the neural network to take the context around an object into consideration during detection. We demonstrate the power of SDR for the problem of 2D bounding bo…
Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large scale variation, complex background interference, and non-uniform density distribution greatly limit the counting accuracy, particularly striking in remote sensing imagery. To mitigate the above issues, this paper proposes a novel framework for dense object counting in remote sensing images, which incorporates a pyramidal scale module (PSM) and a global context module (GCM), dubbed PSGCNet, where PSM is used to adaptively capture…
Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitoring applications. However, performing object detection over large geographies can still be prohibitively expensive due to the high cost of purchasing imagery and compute. Inspired by traditional survey data collection strategies, we propose an approach to estimate object count statistics over large geographies through sampling. Given a cost budget, our method selects a small number of representative areas by sampling …
3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies --- a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference. Taking the inner workings of convolutional neural n…
Federal University of Mato Grosso do Sul（南马托格罗索联邦大学）；University of Western São Paulo（西圣保罗大学）；Xiamen University（厦门大学）；University of Waterloo（滑铁卢大学）
This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement and a Multi-Stage Refinement of the confidence map. The proposed method was evaluated in two counting datasets: tree and car. For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R$^2$) of 0.986. For the car dataset (CARPK and PUCPR+), our method was su…
Localizing and counting large ungulates -- hoofed mammals like cows and elk -- in very high-resolution satellite imagery is an important task for supporting ecological studies. Prior work has shown that this is feasible with deep learning based methods and sub-meter multi-spectral satellite imagery. We extend this line of work by proposing a baseline method, CowNet, that simultaneously estimates the number of animals in an image (counts), as well as predicts their location at a pixel level (localizes). We also propose an methodology for evaluating such models on counting and localization tasks…
Bridging Supervision Gaps: A Unified Framework for Remote Sensing Change Detection
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China（测绘遥感信息工程国家重点实验室，武汉大学，中国）；The University of Tokyo, Tokyo, Japan（东京大学，日本）
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from being deployed in emergencies during which data annotation is either too costly or cannot be obtained fast enough. One popular solution is to use synthetic data for training. Unfortunately, due to domain shift, the resulting models generalize poorly on real imagery. We remedy this shortcoming by training with both synthetic images, along wit…
Citizen science has become a popular tool for preliminary data processing tasks, such as identifying and counting Lunar impact craters in modern high-resolution imagery. However, use of such data requires that citizen science products are understandable and reliable. Contamination and missing data can reduce the usefulness of datasets so it is important that such effects are quantified. This paper presents a method, based upon a newly developed quantitative pattern recognition system (Linear Poisson Models) for estimating levels of contamination within MoonZoo citizen science crater data. Evid…
Estimating the Impact of COVID-19 on Travel Demand in Houston Area Using Deep Learning and Satellite Imagery
Civil and Environmental Engineering, University of Houston（休斯顿大学土木与环境工程系）；Department of Transportation, Logistics and Finance, North Dakota State University（北达科他州立大学交通、物流与金融系）；School of Traffic and Transportation, Shijiazhuang Tiedao University（石家庄铁道大学交通运输学院）
An Efficient Additive Kolmogorov-Arnold Transformer for Point-Level Maize Localization in Unmanned Aerial Vehicle Imagery
Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA（生物系统工程系，威斯康星大学麦迪逊分校）；Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA（农学系，威斯康星大学麦迪逊分校）
Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery
City University of Hong Kong（香港城市大学）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港脑心血管健康工程中心）；Xidian University（西安电子科技大学）；Wake Forest University（维克森林大学）；University of California, Berkeley（加州大学伯克利分校）；Tulane University（杜兰大学）；Lingnan University（岭南大学）
GeoAlignCLIP: Enhancing Fine-Grained Vision-Language Alignment in Remote Sensing via Multi-Granular Consistency Learning
College of Computer Science and Technology, Jilin University, Changchun 130012, China（吉林大学计算机科学与技术学院，长春130012，中国）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University（教育部符号计算与知识工程重点实验室，吉林大学）
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition
Aerospace Information Research Institute, Chinese Academy of Sciences（航天信息研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；Shanghai Jiao Tong University（上海交通大学）；University of Wollongong（沃林戈大学）；SAIS, Shanghai Jiao Tong University（上海交通大学国际关系学院）
RSVLM-QA: A Benchmark Dataset for Remote Sensing Vision Language Model-based Question Answering
School of Computer Science, University of Technology Sydney（技术悉尼大学计算机科学学院）；SEDE, University of Technology Sydney（技术悉尼大学SEDE）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
College of Computer and Information, Hohai University（河海大学计算机与信息学院）；Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；College of Information Science and Technology, Nanjing Forestry University（南京林业大学信息科学与技术学院）；Institute of Forest Resource Information Techniques, Chinese Academy of Forestry（中国林业科学研究院森林资源信息技术研究所）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events
School of Earth Sciences, Zhejiang University（浙江大学地球科学学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Provincial Key Laboratory of Geographic Information Science（浙江省地理信息科学重点实验室）；Key Laboratory of Spatio-temporal Information and Intelligent Services (LSIIS), Ministry of Natural Resources of the People’s Republic of China（国家自然资源部空间时空信息与智能服务重点实验室）
A Benchmark for Multi-Lingual Vision-Language Learning in Remote Sensing Image Captioning
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））；Northwest Institute of Nuclear Technology（西北核技术研究所）
