Remote Sensing Imagery & Artificial Intelligence
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The following presentation was delivered by Robert Morrison, Principal Consultant at Esri Ireland, at the 2019 NICS ICT Conference in October 2019. The presentation focuses on taking a geographic approach to machine learning to help you "see what other's can't". Imagery and remotely sensed data is a valuable resource for many organisations who have made substantial investment obtaining the data. The field of Machine Learning is both broad and deep and is constantly evolving. Using ArcGIS and Machine Learning allows organisations to derive valuable new content. ArcGIS is an open, interoperable platform that allows for the integration of complementary methods and techniques that empower ArcGIS users to solve complex, real-world problems in a fundamentally spatial way. Learn how by combining powerful built-in Image analysis tools with any machine learning package users can benefit from the spatial validation, geo-enrichment and visualisation. See how this Machine Learning is being applied in real world use-cases from marine farming and crime analysis to agriculture and sustainability.
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Research Areas In Geomatics
Geomatics involves gathering, storing, processing, and delivering geospatial information. Key areas include geodesy, surveying, mapping, and photogrammetry. ICT has enabled automation and integration of these areas through technologies like GIS, remote sensing, GPS, and web-based systems. Geomatics research includes spatial data representation, computation, cognition, applications in society, and more. Challenges include implementing geometry concepts digitally and analyzing large geospatial datasets.
Demonstration of super map ai gis technology
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Artificial Intelligence
The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms
jbptunikompp-gdl-agusmulyan-23539-14-isrc&con-d.ppt
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Digital transformation in plant protection leads to
o Increased efficiency: Reduced manual labour, operational costs, improved resource allocation, and optimised workflows.
o Data driven decision making: Farmers can make more informed choices based on data-driven insights, leading to better pest and disease management strategies.
o Automation and predictive analytics: Automation of tasks like pesticide application has reduced human error and resource waste. Predictive analytics models optimise preventive measures.
o Monitoring: Digital solutions enable real-time monitoring by using cell phones.
o Knowledge sharing and innovation: Rapid sharing of knowledge, best practices, and information among farmers, researchers, and stakeholders is possible.
Also, digital transformation opens up avenues for communication among farmers, scientists, and government bodies, resulting in a multitude of indirect benefits: scientists gain better data access, governments improve their policy-making processes, and farmers attain increased crop productivity.
Computer science web
This document summarizes why computer science is a good field of study at DSU. It begins by explaining that computer science involves the study of computer hardware, software, and technology. It then provides examples of subfields within computer science like artificial intelligence, networking, and human-computer interaction. The document highlights job opportunities in computer science, noting that many fast-growing jobs are in IT fields. It also lists local employers of computer science graduates. Finally, it outlines advantages of the computer science program at DSU, such as having PhD faculty, offering internships, encouraging participation in competitions, and providing flexibility to meet student needs.
Forest plantation management system .pdf
1. A forestry company approached a IT solution company to develop a customized forest plantation management system to help monitor and manage their plantations more efficiently due to challenges including difficulty monitoring remote areas, inefficient resource use, and manual data errors.
2. The IT solution company analyzed the forestry company's requirements and processes then developed and implemented a comprehensive system including IoT sensors to collect real-time environmental data, a mobile app for efficient planning and tracking, automated data collection, and real-time notifications.
3. The new system addressed all the forestry company's challenges and improved their ability to monitor plantation growth and health, use resources efficiently, process data accurately, and respond quickly to changes.
Innovation fund themed competition webinar - session 2
Free up personnel through the application of innovative use of machine learning algorithms and artificial intelligence (AI) for military advantage. The RAF requires solutions to manage, analyze, and exploit multiple information sources at pace to gain decision advantage over exponentially growing data in comparison to constrained human analytic capacity. The RAF is interested in combining human-derived models that exploit domain knowledge with machine-derived models requiring large datasets and machine learning to close the gap between data and human capacity to identify the right information.
Artifitial intelligence (ai) all in one
Artificial intelligence (AI) all in one presentation consists of almost all concepts of Artificial intelligence. i.e.
Artificial intelligence, Robotics, ANN, NLP, NLU, History of AI, History, Pakistan, basharat jehan, agriculture university peshawar, Forward and Backward Chaining, Grammers in AI, Morphology, Examples of Expert system, Laws of Robotics,Expert system languages, Syntactic Analysis in NLP, Lecture Notes
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Artificial intelligence : Basics and application in Agriculture
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- 2. Remote Sensing Imagery and Artificial Intelligence Radio Frequency Tracking 3D Buildings Telco Modeling Vegetation Visualization UAV Data Capture Lidar-Based Topo Map Elevation and Feature Extraction Wildfire Imagery Feature Extraction Hydrology Analysis Hurricane Damage Florida Wildfire Aerial Imagery
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- 11. Kolovai Palms
- 12. Train the data
- 13. Export the training data
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- 15. Execute the deep learning model
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