Sökning: "Terrain Classification"

Visar resultat 1 - 5 av 29 uppsatser innehållade orden Terrain Classification.

1. 1. Land cover classification using machine-learning techniques applied to fused multi-modal satellite imagery and time series data

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Anastasia Sarelli; [2024]
   Nyckelord :Geography; GIS; Land Cover Classification; Landsat; Machine Learning; Earth and Environmental Sciences;

   Sammanfattning : Land cover classification is one of the most studied topics in the field of remote sensing, involving the use of data from satellite sensors to analyze and categorize different land surface types. There are numerous satellite products available, each offering different spatial, spectral, and temporal resolutions. LÄS MER

3. 2. Generating an information security classification model for satellite imagery and geographical information

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Marcus Elander; Philip Gunnarsson; [2022]
   Nyckelord :Information security; Machine learning; Satellite imagery; Classification; Risk;

   Sammanfattning : Throughout history, geographical information has been vital in different contexts, such as national security matters, economics, geopolitics, military, and natural resources. Due to the various applications, geographical information has been handled as valuable and sensitive information. LÄS MER

4. 3. Vibration-Based Terrain Classification for an Autonomous Truck

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Lucas Lovén; [2022]
   Nyckelord :Autonomous vehicles; machine learning; surface identification; obstacle identification; signal processing; time series classification; Autonoma fordon; maskininlärning; detektering av underlag; detektering av hinder; signalhantering; tidsserie klassifiering;

   Sammanfattning : This thesis is focused on developing vibration based terrain classification for an autonomous mining truck. The goal is to classify between good and bad gravel roads as well as good and bad asphalt roads. Current literature within vibration based terrain classification has been focused to a great extent on smaller research vehicles. LÄS MER

5. 4. Machine Learning on Terrain Data and Logged Vehicle Data to Gain Insights into Operating Conditions for an Articulated Hauler : Machine Learning on Terrain Data and Logged Vehicle Data to Gain Insights into Operating Conditions for an Articulated Hauler

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Tianren Sun; Yen Chieh Wang; [2022]
   Nyckelord :CNN; Data Driven Manufacturing; Microsoft Azure Maps; Machine; Learning ML ; Road Surface; Topography;

   Sammanfattning : Manufacturers can develop next-generation production and service for their customers by the data gathered and analyzed from customers’ usage conditions. In this research, the operating condition of articular haulers is collected and analyzed through machine learning algorithms to predict the type of operational topographies and road surface. LÄS MER

7. 5. ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Gabriela Olekszyk; [2022]
   Nyckelord :Geography; GIS; Lidar; ALS; Point cloud; Accuracy; Quality; Technology and Engineering;

   Sammanfattning : Gabriela Olekszyk ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand. Lidar (Light Detection and Ranging) data is becoming more widely available and accessible. LÄS MER