U-net super-neural segmentation and similarity calculation to realize vegetation change assessment in satellite imagery
University of Shanghai for Science and Technology（上海理工大学）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Tianjin University（天津大学）；Suffolk University（萨福克大学）
Vegetation is the natural linkage connecting soil, atmosphere and water. It can represent the change of land cover to a certain extent and serve as an indicator for global change research. Methods for measuring coverage can be divided into two types: surface measurement and remote sensing. Because vegetation cover has significant spatial and temporal differentiation characteristics, remote sensing has become an important technical means to estimate vegetation coverage. This paper firstly uses U-net to perform remote sensing image semantic segmentation training, then uses the result of semantic segmentation, and then uses the integral progressive method to calculate the forestland change rate, and finally realizes automated valuation of woodland change rate.
Mapping Woody Vegetation from Multi-Source Imagery and Prediction Fusion for Enhanced Data Efficiency and Accuracy
New South Wales Department of Climate Change, Energy, the Environment and Water（新南威尔士州气候变化、能源、环境和水利部）；Monash University（莫纳什大学）；University of New England（新英格兰大学）；University of Newcastle（纽卡斯尔大学）；Conservation Science Research Group（保护科学研究组）
Faculty of Mathematics and Computer Science（数学与计算机科学学院）
In recent years, the geospatial industry has been developing at a steady pace. This growth implies the addition of satellite constellations that produce a copious supply of satellite imagery and other Remote Sensing data on a daily basis. Sometimes, this information, even if in some cases we are referring to publicly available data, it sits unaccounted for due to the sheer size of it. Processing such large amounts of data with the help of human labour or by using traditional automation methods is not always a viable solution from the standpoint of both time and other resources. Within the pres…
ReflectGAN: Modeling Vegetation Effects for Soil Carbon Estimation from Satellite Imagery
School of Computing, Mathematics, and Engineering, Charles Sturt University（计算机科学与工程学院，查尔斯·斯特劳特大学）；Cooperative Research Centre for High Performance Soils（高性能土壤联合研究中心）；School of Information Technology, Deakin University（信息科技学院，德金大学）；Institute of Innovation, Science and Sustainability, Federation University（创新、科学与可持续性研究所，联邦大学）；Gulbali Institute, Charles Sturt University（古尔巴利研究所，查尔斯·斯特劳特大学）
Hubei University of Technology（湖北工业大学）；Wuhan University（武汉大学）
Land cover classification is a multi-class segmentation task to classify each pixel into a certain natural or man-made category of the earth surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by down sampling or cropping them into small patches less than 512*512 pixels before sending them to a deep neural network. However, down sampling images incurs spatial detail loss, renders small segments hard to discriminate, and reverses the s…
Manchester Metropolitan University（曼彻斯特城市大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Brunel University（布鲁内尔大学）
Spectral-spatial based deep learning models have recently proven to be effective in hyperspectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of "black-box" model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model -- a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-k…
Bangalore Institute of Technology（班加罗尔理工学院）；Indian Institute of Science（印度科学学院）
This work presents a method for semantic segmentation of mango trees in high resolution aerial imagery, and, a novel method for individual crown detection of mango trees using segmentation output. Mango Tree Net, a fully convolutional neural network (FCN), is trained using supervised learning to perform semantic segmentation of mango trees in imagery acquired using an unmanned aerial vehicle (UAV). The proposed network is retrained to separate touching/overlapping tree crowns in segmentation output. Contour based connected object detection is performed on the segmentation output from retrained…
We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. The framework uses data collected before, during, and after the crisis to enable rapid and informed decision making during all phases of disaster response. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster and creates metadata that is transformed into actionable information through web-accessible mapping tools. In part…
Politecnico di Torino（都灵理工大学）；Politecnico Interdepartmental Centre for Service Robotics（都灵理工大学跨部门服务机器人中心）；Università degli Studi di Torino（都灵大学）；Institute of Electronics, Computer and Telecommunication Engineering of the National Research Council of Italy（意大利国家研究委员会电子、计算机与电信工程研究所）
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are gr…
Learning Woody Clearing With Loss Alignment for Zero-Shot Regrowth and Woody Segmentation
New South Wales Department of Climate Change, Energy, the Environment and Water（新南威尔士州气候变化、能源、环境与水利部）；Monash University（莫纳什大学）；University of New England（新英格兰大学）；University of Newcastle（纽卡斯尔大学）
The analysis of satellite imagery will prove a crucial tool in the pursuit of sustainable development. While Convolutional Neural Networks (CNNs) have made large gains in natural image analysis, their application to multi-spectral satellite images (wherein input images have a large number of channels) remains relatively unexplored. In this paper, we compare different methods of leveraging multi-band information with CNNs, demonstrating the performance of all compared methods on the task of semantic segmentation of agricultural vegetation (vineyards). We show that standard industry practice of …
Multi-modal Uncertainty Robust Tree Cover Segmentation For High-Resolution Remote Sensing Images
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家重点实验室）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，德雷塞尔大学）；Swiss Federal Institute for Forest, Snow, and Landscape Research WSL（瑞士森林、雪和景观研究联邦 institute WSL）
Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery
Center for Space and Remote Sensing Research, National Central University（国立中央大学太空及遥测研究中心）；Department of Computer Science and Information Engineering, National Central University（国立中央大学资讯工程学系）
Department of Geosciences and Natural Resource Management, University of Copenhagen（哥本哈根大学地球科学与自然资源管理系）；Department of Mathematical Sciences, University of Copenhagen（哥本哈根大学数学科学系）；Department of Computer Science and Electrical Engineering, University of Rostock（罗斯托克大学计算机科学与电气工程系）；Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）
Monitoring long-term landslide activity is important for risk assessment and land management. Despite the widespread use of open-access 30m Landsat imagery, their utility for landslide detection is often limited when separating landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite …
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）
The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for the landcover classification, especially concerning the vegetation assessment. Despite the usefulness of NIR, common RGB is not always accompanied by it. Modern achievements in image processing via deep neural networks allow generating artificial spectral information, such as for the image colorization problem. In this research, we aim to investigate whether this approach can produce not only visually similar images but also an artificial spectral band that ca…
Feasibility to detect rapid change and disappearance of seagrass: Lessons from nearly 80 years of vegetation change in the Ako, Seto Inland Sea, Japan
Japan Agency for Marine-Earth Science and Technology (JAMSTEC)（日本海洋地球科学技术机构）；The University of Tokyo（东京大学）；Tokyo University of Marine Science and Technology（东京海洋大学）；Shiga University（滋贺大学）
CloudBreaker: Breaking the Cloud Covers of Sentinel-2 Images using Multi-Stage Trained Conditional Flow Matching on Sentinel-1
Computer Science and Engineering（计算机科学与工程）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Institute of Water and Flood Management（水与洪水管理研究所）
A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation
Computer Vision and Learning Lab, IWR, Heidelberg University, Germany（海德堡大学计算机视觉与学习实验室）；AIMMO, Republic of Korea（韩国AIMMO）；Department of Computer Science, TU Darmstadt, Germany（图宾根大学计算机科学系）；Zuse School ELIZA, Germany（德国Zuse学校ELIZA）；Hessian Center for AI (hessian.AI), Germany（黑森人工智能中心）
Beijing University of Chemical Technology（北京化工大学）；Sugon Information Industry Co., Ltd（曙光信息产业有限公司）
The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image, which limits the classification accuracy and universality of their methods. This paper proposed a novel method to take into the information of remote sensing image, i.e., geographic latitude-longitude information. In addition, a dual-branch convolutional neural network (CNN) cl…
