A review of Convolutional Neural Networks in Remote Sensing Image

Effectively analysis of remote-sensing images is very important in many practical applications, such as urban planning, geospatial object detection, military monitoring, vegetation mapping and precision agriculture. Recently, convolutional neural network based deep learning algorithm has achieved a series of breakthrough research results in the fields of objective detection, image semantic segmentation and image classification, etc. Their powerful feature learning capabilities have attracted more attention and have important research value. In this article, firstly we have summarized the basic structure and several classical convolutional neural network architectures. Secondly, the recent research problems on convolutional neural network are discussed. Later, we summarized the latest research results in convolutional neural network based remote sensing fields. Finally, the conclusion has made on the basis of current issue on convolutional neural networks and the future development direction

Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach

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Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.This work is part of the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8, LifeWatch-2019-10-UGR-01 WP-7 and LifeWatch-2019-10-UGR-01 WP-4. This work was also supported by projects A-RNM-256-UGR18, A-TIC-458-UGR18, PID2020-119478GB-I00 and P18-FR-4961. E.G. was supported by the European Research Council grant agreement n° 647038 (BIODESERT) and the Generalitat Valenciana, and the European Social Fund (APOSTD/2021/188). We thank the “Programa de Unidades de Excelencia del Plan Propio” of the University of Granada for partially covering the article processing charge

Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program LifeWatch-2019-10-UGR-01LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8 LifeWatch-2019-10-UGR-01 WP-7 LifeWatch-2019-10-UGR-01 WP-4European Research Council (ERC)European Commission 647038Center for Forestry Research & Experimentation (CIEF) APOSTD/2021/188 A-RNM-256-UGR18 A-TIC-458-UGR18 PID2020-119478GB-I00 P18-FR-496

Implement and Analysis on Current Ecosystem Classification in Western Utah of the United States & Yukon Territory of Canada

The study cases in western Utah of the United States and Yukon Territory of Canada have more natural land and conservative ecosystems in North America. The ecosystem classification of land (ECL) in these two ecoregions had been analyzed and validated through implementation. A full ECL case study was accomplished and examined with eight upper levels of ECOMAP plus ecological site and vegetation stand in Western Utah, the US. Theoretically, applying Köppen climate system classification, Bailey’s Domain and Division were applied to the United States, North America, and world continents. However, Canada’s continental upper level ecoregion framework defined the ecological Mozaic on a sub-continental scale, representing an area of the hierarchical ecological units characterized by interactive and adjusting abiotic and biotic factors. Using Bailey’s Domain as the top level of Canada’s territorial ecoregion was recommended. Eight levels of ELCs were established for Yukon Territory, Canada. Thus, the second study case recommends integrating the ecosystem approaches with Bailey’s upper level ECL, broad ecosystem classification, and objectively defined ecological site in different countries, or ecoregions. Our study cases had exemplified the implementations with a full ELCs in Bailey’s 300 Dry Domain and 100 Polar Domain

A Hierarchical Analysis of Ecosystem Classification With Implementing in Two Continental Ecoregions

Background The ecosystem classification of land (ECL) has been studied for a couple of decades, from the beginning of the perfect organism system “top-down” approach to a reversed “bottom-up” approach by defining a micro-ecological unit. After comparing two cases of the ecosystem classification framework implemented in the different continental ecoregions, the processes were carefully examined and justified.Results Theoretically, Bailey’s upper levels of ECL (Description of the ecoregions of the United States, 2nd ed. Rev and expanded (1st ed. 1980). Misc. Publ. No. 1391 (Rev). Washington DC USDA Forest Service; 1995) were applied to the United States and world continents. For the first time, a complete ECL study was accomplished in Western Utah of the United States, with eight upper levels of ECOMAP (National hierarchical framework of ecological units. U.S. Department of Agriculture, Forest Service, Washington, DC. https://www.researchgate.net/publication/237419014_National_hierarchical... 1993) plus additional ecological site and vegetation stand. China’s Eco-geographic classification was most likely fitted into Bailey’s Ecosystem Classification upper-level regime. With a binary decision tree analysis, it had been validated that the Domains have an empty entity for 500 Plateau Domain between the US and China ecoregion framework. Implementing lower levels of ECL to Qinghai Province of China, based on the biogeoclimatic condition, vegetation distribution, landform, and plant species feature, it had classified the Section HIIC1 into two Subsections (labeled as i, ii), and delineated iia of QiLian Mountain East Alpine Shrub and Alpine Tundra Ecozone into iia-1 and iia-2 Subzones. Coordinately, an Ecological Site was completed at the bottom level.Conclusions (1) It was more experimental processing by implementing a full ECL in the Western Utah of the United States based on the ECOMAP (1993). (2) The empty entity, named as Plateau Domain 500, should be added into the top-level Bailey’s ecoregion framework. Coordinately, it includes the Divisions of HI and HII and the Provinces of humid, sub-humid, semiarid, and arid for China\u27s Eco-Geographic region. (3) Implementing a full ECL in a different continent and integrating the lower level\u27s models was the process that could handle the execution management, interpreting the relationship of ecosystem, dataset conversion, and error correction