Deep Learning and IoT Applications for Remote Sensing

Dear Colleagues,

I am happy to inform you that Remote Sensing has taken on the interesting project of a Special Issue on Deep Learning and IoT-based Remote Sensing Applications in Agriculture, Forestry, and Urban Planning to Big Data Scheme. Rapid evolution in almost all fields is becoming very common due to emerging trends of collaborative and communication technologies. The rapid advancement of technology, deployment, and integration of IoT, cloud computation, artificial intelligence, big data analytics, and future communication networks have been accepted as key enablers for different smart applications in agricultural land management, forestry, and urban applications. Agricultural remote sensing has proven higher spatial accuracy from commercial satellites, and USGS and European Agency-based satellites. Temporal resolutions of satellite imageries also increased as well, along with cloud computation performances through Google Earth Engines.

At present, land management for sustainable intensification is a challenging issue which requires detailed land suitability and vulnerability analysis. How can we construct an appropriate land use management system and forest productivity to adapt to the microclimatic environment regionally? In the recent decades, geographic information systems (GIS) and satellite remote sensing (RS) have become very effective and attracted attention in different fields, such as sustainable agriculture, land use planning, urban planning, and forestry for their spatial coverage. Continuous growth in hardware, software, and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. A good number of smart applications have been made by Internet of Things (IoT) that communicate, extending the boundaries of physical and virtual entities of the world further to link with Big Data Analytics for decision analysis. The deep learning approaches have been used for training and testing of imageries in a variety of projects for decision making involving IoT with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing deep learning may deliver cutting-edge solutions for agricultural remote sensing challenges.

Therefore, the aim of this Special Issue of Remote Sensing is to collect articles (original research papers, review articles, and case studies) to provide insight into the application of deep leaning approach and IoT in satellite remote sensing and GIS datasets to handle big data for generating more faster and accurate solutions in site-specific management of lands which involves monitoring, change detection forest vegetation mapping, and modelling for selecting suitable sites (e.g., flooding and drought) at various spatial and temporal changes.

Deep learning and IoT applications in Remote Sensing is an open Special Issue, welcoming a variety of novel scientific articles including innovative and cutting-edge research using remote sensing techniques and data using different deep learning approaches and IoT from remote sensing platforms (ground truth data, satellite, aircraft, radar, drones, etc.) to the study-related issues in agriculture, forestry, urban planning, and management. The editor invites contributions on social, economic, and legal aspects of agriculture, urban planning, and forest management.

Dr. Ahamed Tofael
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Artificial Intelligence (AI)
• deep learning
• IoT
• cloud computation
• big data
• remote sensing
• agriculture, forestry, urban planning and management