Due to insufficient or difficult to obtain data on development in inaccessible regions, remote sensing data is an important tool for interested stakeholders to collect information on economic growth. To date, no studies have utilized deep learning to estimate industrial growth at the level of individual sites. In this study, we harness high-resolution panchromatic imagery to estimate development over time at 419 industrial sites in the People's Republic of China using a multi-tier computer vision framework. We present two methods for approximating development: (1) structural area coverage estimated through a Mask R-CNN segmentation algorithm, and (2) imputing development directly with visible & infrared radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS). Labels generated from these methods are comparatively evaluated and tested. On a dataset of 2,078 50 cm resolution images spanning 19 years, the results indicate that two dimensions of industrial development can be estimated using high-resolution daytime imagery, including (a) the total square meters of industrial development (average error of 0.021 $\textrm{km}^2$), and (b) the radiance of lights (average error of 9.8 $\mathrm{\frac{nW}{cm^{2}sr}}$). Trend analysis of the techniques reveal estimates from a Mask R-CNN-labeled CNN-LSTM track ground truth measurements most closely. The Mask R-CNN estimates positive growth at every site from the oldest image to the most recent, with an average change of 4,084 $\textrm{m}^2$.
The Ohio State University（俄亥俄州立大学）；Byrd Polar and Climate Research Center（伯德极地与气候研究中心）
Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and …
Renewable energy such as solar power is critical to fight the ever more serious climate change. China is the world leading installer of solar panel and numerous solar power plants were built. In this paper, we proposed a deep learning framework named SolarNet which is designed to perform semantic segmentation on large scale satellite imagery data to detect solar farms. SolarNet has successfully mapped 439 solar farms in China, covering near 2000 square kilometers, equivalent to the size of whole Shenzhen city or two and a half of New York city. To the best of our knowledge, it is the first tim…
From Drone Imagery to Livability Mapping: AI-powered Environment Perception in Rural China
School of Geography and Planning, Sun Yat-Sen University（中山大学地理科学与规划学院）；China Regional Coordinated Development and Rural Construction Institute, Urbanization Institute, Sun Yat-Sen University（中山大学中国区域协调发展与乡村建设研究院、城市化研究院）；Key Laboratory of Rural Digital Technology, Ministry of Housing and Urban-Rural Development（住房和城乡建设部乡村数字化技术重点实验室）；School of Geography and Information Engineering, China University of Geosciences（中国地质大学地理与信息工程学院）
Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multi-spectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly non-linear, inspired by the powerful representation for non-linear relationships of deep neural networks, we introduce multi-scale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and prop…
Geo-informatics and Space Technology Development Agency (GISTDA)（地理信息与空间技术发展局）
Traditional image classification techniques often produce unsatisfactory results when applied to high spatial resolution data because classes in high resolution images are not spectrally homogeneous. Texture offers an alternative source of information for classifying these images. This paper evaluates a recently developed, computationally simple texture metric called Weber Local Descriptor (WLD) for use in classifying high resolution QuickBird panchromatic data. We compared WLD with state-of-the art texture descriptors (TD) including Local Binary Pattern (LBP) and its rotation-invariant versio…
Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image
Northwestern Polytechnical University（西北工业大学）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；ETH Zürich（苏黎世联邦理工学院）；Xi’an Jiaotong University（西安交通大学）
提出零样本 Hipandas 框架，通过三网络耦合、两类低秩先验和两阶段训练，联合利用 PAN 图像完成高光谱去噪与超分辨率，性能优于现有方法。
Estimating forest carbon stocks from high-resolution remote sensing imagery by reducing domain shift with style transfer
School of Geography and Remote Sensing, Guangzhou University（广州大学地理与遥感学院）；Innovation Center for Remote Sensing Big Data Intelligent Applications, Guangzhou University（广州大学遥感大数据智能应用创新中心）
Sun Yat-sen University（中山大学）；Wuhan University（武汉大学）
Impervious surface area is a direct consequence of the urbanization, which also plays an important role in urban planning and environmental management. With the rapidly technical development of remote sensing, monitoring urban impervious surface via high spatial resolution (HSR) images has attracted unprecedented attention recently. Traditional multi-classes models are inefficient for impervious surface extraction because it requires labeling all needed and unneeded classes that occur in the image exhaustively. Therefore, we need to find a reliable one-class model to classify one specific land…
Nowadays, many large-scale land-cover (LC) products have been released, however, current LC products for China either lack a fine resolution or nationwide coverage. With the rapid urbanization of China, there is an urgent need for creating a very-high-resolution (VHR) national-scale LC map for China. In this study, a novel 1-m resolution LC map of China covering $9,600,000 km^2$, called SinoLC-1, was produced by using a deep learning framework and multi-source open-access data. To efficiently generate the VHR national-scale LC map, firstly, the reliable LC labels were collected from three 10-m…
Sub-metre Lunar DEM Generation and Validation from Chandrayaan-2 OHRC Multi-View Imagery Using an Open-Source Pipeline
School of Computer Science and Engineering Manipal University（曼海姆大学计算机科学与工程学院）；Space Applications Centre (SAC) Indian Space Research Organisation (ISRO)（空间应用中心（SAC）印度空间研究组织（ISRO））；Intelligent Robotics Group NASA Ames Research Center（智能机器人小组美国国家航空航天局阿姆斯研究中心）
Super-resolution aims at increasing image resolution by algorithmic means and has progressed over the recent years due to advances in the fields of computer vision and deep learning. Convolutional Neural Networks based on a variety of architectures have been applied to the problem, e.g. autoencoders and residual networks. While most research focuses on the processing of photographs consisting only of RGB color channels, little work can be found concentrating on multi-band, analytic satellite imagery. Satellite images often include a panchromatic band, which has higher spatial resolution but lo…
Tsinghua University（清华大学）；Chinese Academy of Sciences（中国科学院）；University of California, Berkeley（加州大学伯克利分校）；Joint Center for Global Change Studies（全球变化研究联合中心）
The objective of this paper is to evaluate the potential of Gaofen-2 (GF-2) high resolution multispectral sensor (MS) and panchromatic (PAN) imagery on water mapping. Difficulties of water mapping on high resolution data includes: 1) misclassification between water and shadows or other low-reflectance ground objects, which is mostly caused by the spectral similarity within the given band range; 2) small water bodies with size smaller than the spatial resolution of MS image. To solve the confusion between water and low-reflectance objects, the Landsat 8 time series with two shortwave infrared (…
Forest canopy height estimation from satellite RGB imagery using large-scale airborne LiDAR-derived training data and monocular depth estimation
Beijing Normal University（北京师范大学）；CSIRO Environment（英联邦科学与工业研究组织环境部）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Wuhan University（武汉大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7 ETM+) provide low-spatial high-spectral resolution multispectral (MS) or high-spatial low-spectral resolution panchromatic (PAN) images, separately. In order to reconstruct a high-spatial/high-spectral resolution multispectral image volume, either the information in MS and PAN images are fused (i.e. pansharpening) or super-resolution reconstruction (SRR) is used with only MS images captured on different dates. Existing methods do not utilize temporal information of MS and high spatial resolution of PAN images together to …
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China（中国科学院 aerospace information research institute, 北京）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China（中国科学院大学电子电气与通信工程学院, 北京）；School of Mathematics, Southeast University, Nanjing 210096, China（东南大学数学学院, 南京）；Graduate School of Frontier Sciences, the University of Tokyo, Chiba 277-8561, Japan（东京大学前沿科学研究生院, 日本）；RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan（RIKEN 高度智能项目中心, 日本）
A Novel Large Vision Foundation Model (LVFM)-based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management
MCD-Net: A Lightweight Deep Learning Baseline for Optical-Only Moraine Segmentation
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；School of Geographical Sciences, UNNC（地理科学学院）；Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, UNNC（诺丁汉大学宁波校区卓越研究与创新研究所）；Department of Electrical and Electronic Engineering, UNNC（电子与电气工程系）
