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Topic Review
Global Open-Data Remote-Sensing Satellite Missions
The application of global open data remote sensing satellite missions is in the state of rapid growth, ensuring an observation with high spatial and spectral resolution over large areas. Multispectral (Landsat, Sentinel-2, and MODIS), radar (Sentinel-1), and digital elevation model missions (SRTM, ASTER) were analyzed, as the most often used global open data satellite missions, according to the number of scientific research articles published in Web of Science database. Processing methods of these missions’ data consisting of image preprocessing, spectral indices, image classification methods, and modelling of terrain topographic parameters were analyzed and demonstrated. Possibilities of their application in land cover, land suitability, vegetation monitoring, and natural disaster management were evaluated, having high potential in broad use worldwide. Availability of free and complementary satellite missions, as well as the open-source software, ensures the basis of effective and sustainable land use management, with the prerequisite of the more extensive knowledge and expertise gathering at a global scale.
  • 1.5K
  • 12 Nov 2020
Topic Review
Environmental Monitoring Applications
Concerns about global environmental challenges, such as the alarming increase in pollution of oceans, waterways, land, and air, are becoming more and more prevalent in contemporary society. Environmental pollution has evolved into more than a health concern because of global industrialization and mass consumption patterns; it now represents a danger to whole ecosystems. It is critical to comprehend its causes and mitigation strategies. Adequate and timely environmental data are required for risk forecasting and early warning for environmental disasters.
  • 1.5K
  • 23 Nov 2022
Topic Review
Remote Sensing and Deep Learning
The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, is a great candidate for exploiting the potentials of such complex massive data. However, with remote sensing, there are some challenges related to the ground-truth, resolution, and the nature of data that require further efforts and adaptions of deep learning techniques.
  • 1.5K
  • 26 Jan 2021
Topic Review
Application of GIS in Agriculture
GIS technology application in agriculture has gained prominence. The main GIS application areas identified included: crop yield estimation, soil fertility assessment, cropping patterns monitoring, drought assessment, pest and crop disease detection and management, precision agriculture, and fertilizer and weed management. GIS technology has the potential to enhance agriculture sustainability by integrating the spatial dimension of agriculture into agriculture policies. In addition, GIS's potential in promoting evidence-informed decision-making is growing. There is, however, a big gap in GIS application in sub-Saharan Africa. With the growing threat of climate change to agriculture and food security, there is an increased need for the integration of GIS in policy and decision-making in improving agriculture sustainability.
  • 1.4K
  • 09 Sep 2022
Topic Review
The Use of Green Laser in LiDAR Bathymetry
Bathymetric LiDAR technology is a technology used for simultaneous data acquisition regarding the morphology of the bottom of water reservoirs and the surrounding coastal zone, realized from the air, e.g., by plane or drone. Contrary to the air topographic LiDAR, which uses an infrared wavelength of 1064 nm, bathymetric LiDAR systems additionally use a green wavelength of 532 nm. The green laser can penetrate the water, which makes it possible to measure the depth of shallow water reservoirs, rivers, and coastal sea waters within three Secchi depths.
  • 1.4K
  • 13 Jan 2023
Topic Review
Hyperspectral Remote Sensing and Plant
Hyperspectral remote sensing provides image data with very high spectral resolution. This high resolution allows subtle differences in plant health to be recognized. Such a multidimensional data space, generated by hyperspectral sensors, has given rise to new approaches and methods for analyzing hyperspectral data.
  • 1.3K
  • 31 Jan 2022
Topic Review
LCM Model
Cellular Automata-Markov chain (CA-Markov) model is a general method that has been widely used to predict LUCC. However, the process of this treditional model is subjective and stochastic, which makes it's modeling capacity limited. For precisely detecting the LUCC and their driving factors, we introduced the Logistic regression method to integrate with the treditional CA-Markov model (Logistic-CA-Markov model, LCM), to improve the preformance of modeling LUCC. This model would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis.
  • 1.2K
  • 29 Oct 2020
Topic Review
Image-Based Obstacle Detection Methods
Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. These various image-based obstacle detection techniques include Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles (MAVs). The techniques were divided into monocular and stereo. The former uses a single camera, while the latter makes use of images taken by two synchronised cameras. Monocular obstacle detection methods are discussed in appearance-based, motion-based, depth-based, and expansion-based categories. Monocular obstacle detection approaches have simple, fast, and straightforward computations. Thus, they are more suited for robots like MAVs and compact UAVs, which usually are small and have limited processing power. On the other hand, stereo-based methods use pair(s) of synchronised cameras to generate a real-time 3D map from the surrounding objects to locate the obstacles. Stereo-based approaches have been classified into Inverse Perspective Mapping (IPM)-based and disparity histogram-based methods. Whether aerial or terrestrial, disparity histogram-based methods suffer from common problems: computational complexity, sensitivity to illumination changes, and the need for accurate camera calibration, especially when implemented on small robots.
  • 1.2K
  • 24 Oct 2022
Topic Review
Google Earth Engine and Artificial Intelligence
Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. 
  • 1.1K
  • 15 Aug 2022
Topic Review
Deep Learning-Based Change Detection
Change detection based on remote sensing images plays an important role in the field of remote sensing analysis, and it has been widely used in many areas, such as resources monitoring, urban planning, disaster assessment, etc. With the improved spatial resolution of remote sensing images, many deep learning methods have been proposed for aerial and satellite image change detection. Depending on the granularity of the detection unit, these methods can be roughly classified into two main categories : scene-level methods (SLCDs) and region-level methods (RLCDs). These two categories are not necessarily independent of each other, and sometimes, the same change detection process may be present in different methods simultaneously.
  • 1.1K
  • 01 Apr 2022
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