digital twin and
Building a convergence ecosystem of AI agents
We acquire field data using sensors optimized for the purpose, and provide precise analysis and predictions using our own AI model.
Beyond simple photography, we directly derive the insights needed for decision-making.
Utilization of unmanned aerial vehicles (UAVs) Building digital twin spatial information
We build a digital twin environment linked to drone data.
Construction of high-precision spatial information
Quickly acquire nationwide orthoimages and 3D models with RTK-based cm-level accuracy. Significantly reduces cost and time compared to existing aerial surveying.
- Automatic generation of DSM/DTM
- GSD 1.0cm level high-resolution data
- Create contour and 3D terrain relief data layers
Crop growth status analysis
NDVI, NDRE, and NDWI are simultaneously calculated using multi-spectral spectra to precisely diagnose crop growth status.
- Crop health diagnosis
- Automatic detection of pest surveillance areas
- Advance prediction of expected yield
LiDAR-based high-precision terrain analysis and 3D modeling
Using high-density point clouds, we extract pure ground elevation through bushes and vegetation, and perform accurate volume calculations of the target site and 3D modeling of the structure.
- Creation of a vegetation-transparent DEM
- Automatic calculation of cut and fill earthwork volume
- High-precision 3D modeling of structures and buildings
Disaster safety and environmental monitoring based on thermal imaging and multiple sensors
Using high-resolution thermal imaging sensors and specialized equipment, we diagnose defects in industrial facilities, detect forest fires and air/water pollution at an early stage, and monitor wide areas in real time.
- Solar panel precision deterioration diagnosis
- Intelligent forest fire and fire surveillance
- Wide-area air and water quality environmental monitoring
key predictions Intelligent solution that automatically derives
The model accuracy continues to improve as field data accumulates.
Deep learning-based AI crop classification and growth prediction
By applying the latest deep learning segmentation model optimized for multispectral and hyperspectral image data, it automatically and precisely identifies crop types and predicts growth patterns even in complex terrain.
Multi-Sensor Combined Machine Learning Yield Prediction Model
Harvest time and expected harvest volume are quantitatively simulated through a multi-modal algorithm that combines LiDAR's 3D structural information (vegetation height and volume) and multispectral vitality data.
Automatic detection of anomalies and defects based on notice function
Thermal patterns appearing in high-resolution thermal images are analyzed in real time using a convolutional neural network (CNN) model to automatically classify micro-defects and deterioration conditions within industrial facilities and major infrastructure at the level of human experts and generate maintenance reports.
AI-based time series change detection and anomaly prediction
A deep learning model compares and analyzes time-series spatial data periodically acquired from the same area to track subtle deformations of the ground surface, vegetation conditions, and structures and proactively predict disaster risks.
Experience advanced drone technology services, such as aerial surveying and inspection, directly from experts.
Wide-area air and water quality environmental monitoring
Precise data solutions that fill gaps in the field.
Experience advanced drone technology services, such as aerial surveying and inspection, directly from experts.
