New Zealand Imagery

Description

The New Zealand Imagery dataset consists of New Zealand's publicly owned aerial and satellite imagery, which is freely available to use under an open licence. The dataset ranges from the latest high-resolution aerial imagery down to 5cm in some urban areas to lower resolution satellite imagery that provides full coverage of mainland New Zealand, Chathams and other offshore islands. It also includes historical imagery that has been scanned from film, orthorectified (removing distortions) and georeferenced (correctly positioned) to create a unique and crucial record of changes to the New Zealand landscape.All of the imagery files are Cloud Optimised GeoTIFFs using lossless WEBP compression for the main image and lossy WEBP compression for the overviews. These image files are accompanied by STAC metadata. The imagery is organised by region and survey.

Update Frequency

New imagery will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps.

License

Toitū Te Whenua Land Information New Zealand does not own the copyright for all of the imagery that is available in this bucket. Licensors are contained in the STAC Collection file accompanying each imagery survey. Licensed for re-use under "CC-BY-4.0".

Documentation

https://github.com/linz/imagery

Managed By

Toitū Te Whenua Land Information New Zealand

See all datasets managed by Toitū Te Whenua Land Information New Zealand.

Contact

imagery@linz.govt.nz

How to Cite

New Zealand Imagery was accessed on DATE from https://registry.opendata.aws/nz-imagery. Licensed by (insert the licensor) for re-use under CC-BY-4.0.

Usage Examples

Tutorials

• Browsing the s3://nz-imagery bucket by Toitū Te Whenua Land Information New Zealand
  
• Imagery survey naming and path conventions by Toitū Te Whenua Land Information New Zealand
  

Tools & Applications

• LINZ Basemaps by Toitū Te Whenua Land Information New Zealand
  
• LINZ Data Service by Toitū Te Whenua Land Information New Zealand
  
• LINZ Topographic Workflows - Bulk imagery processing with Kubernetes and Argo Workflows by Toitū Te Whenua Land Information New Zealand
  

Publications

• A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction by Guangming Wu, Zhiling Guo, Xiaodan Shi, Qi Chen, Yongwei Xu, Ryosuke Shibasaki and Xiaowei Shao
  
• A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning by T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla & Kofi Boakye
  
• Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response by Grant D. Pearse, Michael S. Watt, Julia Soewarto and Alan Y. S. Tan
  
• Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network by Yaning Yi, Zhijie Zhang, Wanchang Zhang, Chuanrong Zhang, Weidong Li and Tian Zhao