TheMapper
Professional IT Consultant
3D Laser Scanning
Drone Mapping
GIS & Spatial Data Science
GNSS Surveying
SCAN to BIM
IT Consultant
Software Development
System Integration
Data Modeling
Knowledge Transfer
Machine Learning
Machine learning for spatial applications involves using algorithms and statistical models to analyze, model, and predict spatial data. These applications are crucial in fields such as geography, urban planning, environmental monitoring, and more.
01
Preventive Maintenance : RAIL
To develop a preventive maintenance model for rail tracks using the data you mentioned (rail gauge, cross level, longitudinal level, versine for alignment, and twist), you need to treat these measurements as independent variables (features) and develop a target variable (dependent variable) that indicates the maintenance status. The target variable could be a binary indicator (e.g., 0 for no maintenance needed, 1 for maintenance needed) or a continuous variable representing the degradation level.
02
Modeling
Anomaly Detection Algorithms: Use models like Isolation Forest or DBSCAN to detect outliers in the time-series data. An anomaly refers to a data point or pattern that significantly deviates from the expected behavior or norm. Anomalies can indicate rare or unusual events, which may or may not be indicative of problems.
Rail Application
There may be no better way to communicate what we do than through images. As you browse our site, take a few moments to let your eyes linger here, and see if you can get a feel for our signature touch.
