This article aims to implement a prototype screening system to identify flooding-related photos from social media. These photos, associated with their geographic locations, can provide free, timely, and reliable visual information about flood events to the decision-makers. This screening system, designed for application to social media images, includes several key modules: tweet/image downloading, flooding photo detection, and a WebGIS application for human verification. In this study, a training dataset of 4800 flooding photos was built based on an iterative method using a convolutional neural network (CNN) developed and trained to detect flooding photos. The system was designed in a way that the CNN can be re-trained by a larger training dataset when more analyst-verified flooding photos are being added to the training set in an iterative manner. The total accuracy of flooding photo detection was 93% in a balanced test set, and the precision ranges from 46–63% in the highly imbalanced real-time tweets. The system is plug-in enabled, permitting flexible changes to the classification module. Therefore, the system architecture and key components may be utilized in other types of disaster events, such as wildfires, earthquakes for the damage/impact assessment.

Coastal wetlands contribute greatly to our coasts economically and ecologically. The utility of coastal wetland vegetation, along with the multitude of dynamic forces they encounter, suggests the need of regular monitoring for sustainable management. While traditional in situ survey methods and remote sensing from space and manned platforms have provided means to monitor and study the coastal zone thus far, the recent developments of small unmanned aerial systems (sUAS) fill a small void between traditional in situ survey methods and the high spatial resolution of manned aircraft imagery. As an on-demand personal remote sensing device, an sUAS can be deployed over coastal regions at a low cost and with very fine spatial resolution (i.e. 1–10 cm) imagery and corresponding spatial accuracy. Though an sUAS provides many benefits, recent literature documents several shortcomings and limitations to using them for coastal wetland vegetation research, including changing tides, lighting conditions and legal restrictions on flying. This study reviewed all coastal wetland vegetation-related studies that included an sUAS as a mapping tool to document the current state of the field. Current practices, successes, and limitations are described, and future directions for the field are discussed. Coastal managers and researchers alike will be able use this comprehensive review to determine how to best approach future studies of diverse coastal vegetation.

The trend in spatial data accuracy has gone from meters to decimeters and even centimeter levels in the last two decades. In large part, the centimeter(s) level spatial accuracies of geospatial products result from the use of LiDAR data and both low altitude manned and unmanned aerial systems for imagery and for topographic and surface models. All these sources are dependent on high precision/accuracy GNSS technologies to achieve such accuracies. The cost of L1/L2 receivers capable of centimeter(s) level position accuracy has rapidly, in the last year, decreased from over $15k – $20k to (in early 2019) to about $300 (in 2020). Such recent low costs provide an economically affordable revolution in the use of centimeter(s) level accuracies in aerial remote sensing, ground support, field data collections, and classroom instruction with GNSS RTK technologies. Except for the marketing literature little is known about their performance in the typical applications of the remote sensing and GIS communities. How accurate are the new low-cost dual-frequency multi-constellation receivers? What is the reliability in typical landscapes and mountainous landscapes? Answers to these questions are important to control the bundle-adjustment in SfM approaches using ground control points, setting confidence limits in topographic change detection, collecting ground reference data, and evaluating orthoimagery. The problem of assessing performance in typical applications is the lack of a reference source of higher precision and accuracy than the low-cost GNSS receivers themselves. In this study, a low-cost receiver/antenna was evaluated using height modernization monuments in the National Geodetic Survey network. These monuments are typically of sub-centimeter positional accuracy. Thirty-six monuments in four piedmont counties of South Carolina were surveyed (in RTK FIX mode). Resulting accuracies for these typical environments were 2.2-cm RMSE in both horizontal and height dimensions. The 95% confidence level accuracies for the horizontal and height dimensions were 3.7-cm and 4.2-cm (95%), respectively. Performance tests in the South Carolina mountains revealed numerous issues with the low cost survey grade GNSS receiver (cellular connections, availability of reference sites, and satellite signal occlusion from mountains) that also plagues more expensive receivers.

In the United States of America (USA), the Federal Aviation Administration (FAA) maintains operational control of the national airspace and the aircraft in it – including small unmanned aerial systems (sUAS). However, states and local jurisdictions can regulate collection of imagery/data and where the operator of the aircraft is located. Surprisingly, numerous states have enacted laws that impact unmanned aircraft operations in the national airspace. The proliferation of state statutes regulating sUAS operations and imagery/data collections has resulted in a patchwork quilt of laws affecting the academic or researcher using sUAS in the United States for image collections. These legal challenges are often known in a scientist’s home state but unknown and challenging to operate in another state. This research examined the state-level statutes (currently as of 2020) that govern sUAS operation and associated image collection in the United States as they would apply to academic research/instruction, in particular. All state statutes were categorized into the preparatory, operations, image/data collection regulations and the geographic space affected. The chronology, of when and by state, the laws enacted were also examined. These regulations are presented with interpretation of how the sUAS operator is affected and the possible exemptions available for university personnel lawfully operating a sUAS in their home state and in a non-home state. Problematic issues, such as the restriction of overflights over critical facilities, when the locations of these type facilities are unknown (because of security), are also presented and discussed. Recommendations on how an academic/researcher can qualify for an exemption to statute restrictions are provided.