GIS Services
GIS Avenew provides a range of GIS services to our clients spread across every continent.
GIS Avenew provides a range of GIS services to our clients spread across every continent.
The GIS services furnished by GIS Avenew allow for the visualization of geographic data, analysis of spatial relationships, and efficient data management.
The GIS data conversions ensure data from different sources are merged in a unified format, which then makes it easy to access, analyze, and utilize.
Digital GIS Data Creation and Conversion
GIS data creation and conversion of 2D and 3D are common processes of embedding geographical data into a file.
GIS data conversion services from GIS Avenew are specially crafted for mapping services.
We are serving numerous clients with our GIS data conversion outsourcing in industries like telecom, oil, and gas, real estate, etc.
Image processing of Remotely Sensed data
GIS Avenew provides high-quality satellite imagery and processing of remotely sensed data for analysis and provides support for GIS and other mapping applications.
We specialize in visual data collection, producing seamless mosaics, digital elevation models, and 3D digital terrain models for our clients.
Geospatial data management & Migration
Updated systems or new industry standards may need specific changes to your spatial data.
GIS Avenew’s automated, rules-based approach helps you reduce the time and cost of data migration and transformation. Intelligent manipulation is easier with our technology.
Cartographic Services
We work with you to produce endless maps you imagine. Right from concept to delivery, our cartographic services are the most efficient and cost-effective solutions for you.
You can use your maps (provided by us), copyright them, and use them wherever you want to make your products the best in your industry.
Image Detection using Tensor flow
Accurate detection and recognition of traffic lights is a crucial part of the development of such vehicles. The concept enables autonomous cars to automatically detect traffic lights which involves the least amount of human interaction.
This enables the ability of the TensorFlow* Object Detection API to solve a real-time problem such as traffic light detection. The experiment uses the Microsoft Common Objects in Context (COCO) pre-trained model called Single Shot Multibox Detector MobileNet from the TensorFlow Zoo for transfer learning. Intel® Xeon® processor-based machines were used for the study. When it ends, we could obtain 90% accuracy in identifying the traffic signals.
HD Maps Processing
HD map production process for autonomous car simulation is an open-source HD map project for autonomous driving. The precision map production process being: collection, production, labeling, and saving.
HD Maps processing mainly uses LIDAR data to accelerate map making process by using following 4 steps.
- Collection
First, we use a car equipped with lidar, camera, GPS and IMU as map collection vehicle to collect maps. Currently, open source dataset is used. We plan to provide the recommended hardware and toy cars very soon.
- Production
We need to generate street and building models based on the collected point cloud data, a three-dimensional model of the entire street is obtained.
- Labeling
After a 3D model of the map is completed, this part of the map is called a point cloud map. To get a lane line, traffic lights and traffic sign information, we also need to label the map. At present, labeling is mainly done manually. We will provide labeling tools. The labeled map is called an HD map.
The two most important questions in map labels are:
- Saving
The process of preserving HD maps is divided into two parts, first is the format of the map, and the then comes the layers of the map.
Format: The format of the map is currently open drive. In short, the project hopes to have a unified labeling format.
Layers: The map is divided into 3 layers: base layer, positioning layer and dynamic layer.