Nebraska NativeGEM (Geospatial Extension Model)

The UNO Aviation Monograph Series, UNOAI Report 04-3, Nebraska NativeGEM (Geospatial Extension Model) - February 2004 - UNO Aviation Institute, University of Nebraska at Omaha

An Assessment of Remote Sensing Applications in Transportation

Remote sensing is an innovative science and technology that is aiding in numerous modes of transportation. Almost every aspect of transportation can benefit from utilizing imagery and data. Specifically, these technologies can be applied to planning, environmental impact assessment, hazard and disaster response, infrastructure management, traffic assessment, and homeland security planning (“Transportation and Remote Sensing,” 1999). The United States transportation system is a critical component of our economy and mobility (Williamson, Morain, Budge, & Hepner, 2002). There are millions of miles of roadways and bridges to monitor and maintain. In addition, remote sensing can be utilized towards the development and planning of new infrastructure and transportation systems. Remote sensing provides the unique ability to detect changes in our transportation system on a real-time basis. Imagery can be collected from multiple platforms, including satellite, aircraft-based, and ground-based, which allows data collection to be tailored to a particular transportation application. This paper will provide an overview of some of the potential applications of remote sensing in transportation. Due to the broad scope of this topic, several modes will not be discussed including aviation and marine. The main focus will be on ground transportation, infrastructure, and homeland security as it relates to transportation applications. Emerging technologies, such as hyperspectral remote sensing and LIDAR, will also be discussed. In addition, the Nebraska Airborne Remote Sensing Facility, one of only a few operating in the United States will be described. Two tribal communities in Nebraska are utilizing the data collected from the facility to address transportation issues

Application of Satellite Sensing to Agricultural Research and Development

Overview done by the FAO Remote Sensing Unit for TAC of the state of the art of remote sensing applied to agricultural research and development with emphasis on satellite sensing and potential international cooperative programs to capitalize on the technology. Agenda document presented at TAC's Tenth Meeting, July 1975

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin