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. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.

A segmentation and hierarchical classification approach applied to QuickBird multispectral satellite data was implemented, with the goal of delineating residential land use polygons and identifying low and high socio‐economic status of neighbourhoods within Accra, Ghana. Two types of object‐based classification strategies were tested, one based on spatial frequency characteristics of multispectral data, and the other based on proportions of Vegetation–Impervious–Soil sub‐objects. Both approaches yielded residential land‐use maps with similar overall percentage accuracy (75%) and kappa index of agreement (0.62) values, based on test objects from visual interpretation of QuickBird panchromatic imagery.

This paper presents methods to test whether map error can explain the observed differences between two points in time among categories of land cover in maps. Such differences may be due to two reasons: error in the maps and change on the ground. Our methods use matrix algebra: (1) to determine whether error can explain specific types of observed categorical transitions between two maps, (2) to represent visually the differences between the maps that error cannot explain, and (3) to examine how the results are sensitive to possible variation in map error. The methods complement conventional accuracy assessment because they rely on standard confusion matrices that use either a random or a stratified sampling design. We illustrate the methods with maps from 1971 and 1999, which show seven land-cover categories for central Massachusetts. The methods detect four transitions from agriculture, range, forest, and barren in 1971 to built in 1999, which a 15 percent error cannot explain. Sensitivity analysis reveal...

Unmanned airborne systems (UAS), particularly the class of UAS referred to as small-unmanned airborne systems (S-UAS), have the potential to revolutionize the science, practice, and role of remote sensing. S-UAS-collected remote sensing data differ from that acquired from larger airborne and space-borne platforms in myriad ways. To provide an indication of the novel remote sensing capabilities that S-UAS are poised to enable and identify research priorities for realizing the full potential of remote sensing from these novel platforms, characteristics of S-UAS platforms and their impact on data and information products are analysed in the context of remote sensing model and the remote sensing communication model. Results indicate that S-UAS will not only enable a range of novel remote sensing capabilities but also present clear challenges to the remote sensing community. These challenges, including increased data volume, a paucity of appropriate analysis approaches, and restrictions on autonomous operation (both regulatory and technological), point towards several near-term research priorities.