Due to limited resources and environmental pollution, monitoring the geological environment has become essential for many countries’ sustainable development. As various high-resolution remote-sensing (RS) imaging platforms are continuously available, the remote sensing of the geological environment (GERS) provides a fine-grain, all-weather, and low-cost method for identifying geological elements. Mainstream machine learning (ML) and deep learning (DL) methods can extract high-level high-dimensional semantic information and thus supply an efficient tool for high-precision classification and recognition in many fields. Therefore, the integration of advanced methods and multi-source RS images for GERS interpretation has achieved remarkable breakthroughs during the past decades. However, to the best of our knowledge, a systematic survey of the advances of GERS interpretation regarding ML and DL methods is still lacking. Through the collection of extensive published research in this area, this survey outlines and analyzes the challenges, progress, and promising directions of GERS interpretation. Specifically, the main challenges and difficulties in identifying GERS elements are first summarized in four aspects: sufficient element characteristics and variations, complex context disturbance, RS image quality and types, and other limitations in GERS interpretation. Second, we systematically introduce various RS imaging platforms and advanced ML and DL methods for GERS interpretation. Third, the research status and trends of several GERS applications, including their use for lithology, soil, water, rock glacier, and geological disaster, are ultimately collected and compared. Finally, potential opportunities for future research are discussed. After the systematic and comprehensive review, the conclusive findings suggest that longtime large-scale GERS interpretation and corresponding change pattern analysis will be a significant future direction to meet the needs of environment improvement and sustainable development. To complete the above goals, a fusion of satellite, airplane, environmental monitoring, geological survey, and other types of data will provide enough discriminative information, and expert knowledge, GIS, and high-performance computing techniques will be helpful to improve the efficiency and generalizability of ML and DL methods for processing the multi-platform RS data. 

A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities

Nearshore estimates of bathymetry are crucial for understanding coastal processes. However, current passive remote sensing methods for estimating bathymetry require in situ depth measurements to train inversion models, which can be difficult or impossible to obtain in many areas. To address this issue, we investigated the fusion of range measurements from the advanced topographic laser altimeter system (ATLAS) aboard the NASA ICESat-2 satellite, and multispectral satellite imagery from European Space Agency (ESA) Sentinel-2 using two common bathymetric inversion algorithms. The active ranging capability of the ATLAS green (532-nm) laser has been shown to generate returns of up to 38-m depth in optically clear waters, providing depth measurements to constrain passive bathymetric inversion results. Data acquired in November 2018 over the nearshore in Destin, FL, USA, offer a proof of concept for this approach. The results of the bathymetric inversion were quantitatively assessed by comparison with airborne bathymetric LiDAR collected using the U.S. Army Corps Coastal Zone Mapping and Imaging LiDAR (CZMIL) system in October–November 2018. Overall, the results of the bathymetric inversion compared with the CZMIL data have a root mean square error (RMSE) of 0.35 m in waters with similar turbidity and bottom reflectivity, and demonstrate that a combination of ICESat-2 depth observations with Sentinel-2 multispectral imagery can estimate seamless nearshore bathymetry for optically clear coastal waters.

Nearshore Bathymetry From Fusion of Sentinel-2 and ICESat-2 Observations

CZMIL is a new airborne mapping and imaging system designed to simultaneously produce high resolution 3D images
of the beach and shallow water seafloor, and to achieve benthic classification and water column characterization. It is
designed to have high performance in shallow, turbid waters. The Data Acquisition System (DAS) is composed of a new
bathymetric lidar integrated with a commercial imaging spectrometer and digital metric camera. The Data Processing
System (DPS) employs new algorithms and software designed to automatically produce environmental image products
by combining data from the three sensors within a data fusion paradigm. CZMIL is specifically designed to meet the
requirements of the USACE Coastal Mapping Program, and is scheduled to enter field trials in the spring of 2011.

Overview of the coastal zone mapping and imaging lidar (CZMIL): a new multisensor airborne mapping system for the U.S. Army Corps of Engineers

Rapid lake Michigan shoreline changes revealed by UAV LiDAR surveys

This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (DDWI). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a >99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.

https://www.mdpi.com/2072-4292/14/3/557/pdf?version=1643106463

A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery

CZMIL is an integrated lidar-imagery system and software suite designed for highly automated generation of physical and environmental information products for coastal zone mapping in the framework of the US Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP). This paper presents the results of CZMIL system validation in turbid water conditions along the Gulf Coast of Mississippi and in relatively clear water conditions in Florida in late spring 2012. Results of the USACE May-October 2012 mission in Green Bay, WI and Lake Erie are presented. The system performance tests show that CZMIL successfully achieved 7-8m depth in Mississippi with Kd =0.46m-1 (Kd is the diffuse attenuation coefficient) and up to 41m in Florida when Kd=0.11m-1. Bathymetric accuracy of CZMIL was measured by comparing CZMIL depths with multi-beam sonar data from Cat Island, MS and from off the coast of Fort. Lauderdale, FL. Validation demonstrated that CZMIL meets USACE specifications (two standard deviation, 2σ, ~30 cm). To measure topographic accuracy we made direct comparisons of CZMIL elevations to GPS-surveyed ground control points and vehicle-based lidar scans of topographic surfaces. Results confirmed that CZMIL meets the USACE topographic requirements (2σ, ~15 cm). Upon completion of the Green Bay and Lake Erie mission there were 89 flights with 2231 flightlines. The general hours of aircraft engine time (which doesn't include all transit/ferry flights) was 441 hours with 173 hours of time on survey flightlines. The 4.8 billion (!) laser shots and 38.6 billion digitized waveforms covered over 1025 miles of shoreline.

CZMIL (coastal zone mapping and imaging lidar): from first flights to first mission through system validation

The Coastal Zone Mapping and Imaging Lidar (CZMIL) 1 system is a new generation airborne remote sensing system. The multi-sensor system integrates a lidar system, hyperspectral camera, digital camera, and a positioning system together to seamlessly record data over topographic and bathymetric environments. The recorded data is post-processed using data fusion algorithms to generate image products. In order to amplify the amount of information contained within the data fed to the algorithms, the arch itecture of the lidar receiver is designe d using numerous techniques to maximize the quality of the recorded data. The receiver architecture employs commercial photomultiplier tubes operated in a logarithmic mode coupled to 10bit, 1GHz analog to digital converters. This architecture achieves sufficient dynamic range to support operating the system in both terrestrial a nd ocean environments. The multi-channel design of the lidar system requires the digitization of 9 channels of optical return signal data . The resulting large data volume necessitated design of a novel data reduction strategy. These important aspects of the lidar receivers design are presented. The strategies illustrate how the receivers architecture is de signed to optimize the fidelity of the recorded data. Keywords: Lidar, photomultiplier tube, digitizer, dynamic range, return signal, data, storage

Proposed lidar receiver architecture for the CZMIL system

CZMIL is an integrated lidar-imagery sensor system and software suite designed for the highly automated generation of physical and environmental information products for mapping the coastal zone. This paper presents the results of CZMIL system validation in turbid water conditions on the Gulf Coast of Mississippi and in relatively clear water conditions in Florida in late spring 2012. The system performance test shows that CZMIL successfully achieved 7-8m depth in K d =0.46m -1 (K d is the diffuse attenuation coefficient) in Mississippi and up to 41m when K d =0.11m -1 in Florida. With a seven segment array for topographic mode and the shallow water zone, CZMIL generated high resolution products with a maximum pulse rate of 70 kHz, and with 10 kHz in the deep water zone. Diffuse attenuation coefficient, bottom reflectance and other environmental parameters for the whole multi km 2 area were estimated based on fusion of lidar and CASI-1500 hyperspectral camera data.

Coastal Zone Mapping and Imaging Lidar (CZMIL): first flights and system validation

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CZMIL (coastal zone mapping and imaging lidar): from first flights to first mission through system validation

Proposed lidar receiver architecture for the CZMIL system

Coastal Zone Mapping and Imaging Lidar (CZMIL): first flights and system validation