Performance Evaluation of Geospatial Images based on Zarr and Tiff
- URL: http://arxiv.org/abs/2411.11291v1
- Date: Mon, 18 Nov 2024 05:34:31 GMT
- Title: Performance Evaluation of Geospatial Images based on Zarr and Tiff
- Authors: Jaheer Khan, Swarup E, Rakshit Ramesh,
- Abstract summary: This evaluate the performance of geospatial image processing using two distinct data storage formats: Zarr and TIFF.
Traditional Tagged Image File Format is mostly used because it is simple and compatible but may lack by performance limitations while working on large datasets.
Zar is a new format designed for the cloud systems,that offers scalability and efficient storage with data chunking and compression techniques.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This evaluate the performance of geospatial image processing using two distinct data storage formats: Zarr and TIFF. Geospatial images, converted to numerous applications like environmental monitoring, urban planning, and disaster management. Traditional Tagged Image File Format is mostly used because it is simple and compatible but may lack by performance limitations while working on large datasets. Zarr is a new format designed for the cloud systems,that offers scalability and efficient storage with data chunking and compression techniques. This study compares the two formats in terms of storage efficiency, access speed, and computational performance during typical geospatial processing tasks. Through analysis on a range of geospatial datasets, this provides details about the practical advantages and limitations of each format,helping users to select the appropriate format based on their specific needs and constraints.
Related papers
- Image-GS: Content-Adaptive Image Representation via 2D Gaussians [52.598772767324036]
We introduce Image-GS, a content-adaptive image representation based on 2D Gaussians radiance.<n>It supports hardware-friendly rapid access for real-time usage, requiring only 0.3K MACs to decode a pixel.<n>We demonstrate its versatility with several applications, including texture compression, semantics-aware compression, and joint image compression and restoration.
arXiv Detail & Related papers (2024-07-02T00:45:21Z) - SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs [81.2396059480232]
SceneGraphLoc learns a fixed-sized embedding for each node (i.e., representing an object instance) in the scene graph.
When images are leveraged, SceneGraphLoc achieves performance close to that of state-of-the-art techniques depending on large image databases.
arXiv Detail & Related papers (2024-03-30T20:25:16Z) - Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation [9.161203553842787]
We present Img2Loc, a novel system that redefines image geolocalization as a text generation task.
Img2Loc first employs CLIP-based representations to generate an image-based coordinate query database.
It then uniquely combines query results with images itself, forming elaborate prompts customized for LMMs.
When tested on benchmark datasets such as Im2GPS3k and YFCC4k, Img2Loc not only surpasses the performance of previous state-of-the-art models but does so without any model training.
arXiv Detail & Related papers (2024-03-28T17:07:02Z) - GeoCLIP: Clip-Inspired Alignment between Locations and Images for
Effective Worldwide Geo-localization [61.10806364001535]
Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth.
Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task.
We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
arXiv Detail & Related papers (2023-09-27T20:54:56Z) - CSP: Self-Supervised Contrastive Spatial Pre-Training for
Geospatial-Visual Representations [90.50864830038202]
We present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images.
We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images.
CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
arXiv Detail & Related papers (2023-05-01T23:11:18Z) - Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation [2.3020018305241337]
We present a simplified but effective architecture based on contrastive learning with symmetric InfoNCE loss.
Our framework consists of a narrow training pipeline that eliminates the need of using aggregation modules.
Our work shows excellent performance on common cross-view datasets like CVUSA, CVACT, University-1652 and VIGOR.
arXiv Detail & Related papers (2023-03-21T13:49:49Z) - Deep Visual Geo-localization Benchmark [42.675402470265674]
We propose a new open-source benchmarking framework for Visual Geo-localization (VG)
This framework allows to build, train, and test a wide range of commonly used architectures.
Code and trained models are available at https://deep-vg-bench.herokuapp.com/.
arXiv Detail & Related papers (2022-04-07T13:47:49Z) - Variable-Rate Deep Image Compression through Spatially-Adaptive Feature
Transform [58.60004238261117]
We propose a versatile deep image compression network based on Spatial Feature Transform (SFT arXiv:1804.02815)
Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps.
The proposed framework allows us to perform task-aware image compressions for various tasks.
arXiv Detail & Related papers (2021-08-21T17:30:06Z) - Gigapixel Histopathological Image Analysis using Attention-based Neural
Networks [7.1715252990097325]
We propose a CNN structure consisting of a compressing path and a learning path.
Our method integrates both global and local information, is flexible with regard to the size of the input images and only requires weak image-level labels.
arXiv Detail & Related papers (2021-01-25T10:18:52Z) - Learning to Localize Through Compressed Binary Maps [83.03367511221437]
We learn to compress the map representation such that it is optimal for the localization task.
Our experiments show that it is possible to learn a task-specific compression which reduces storage requirements by two orders of magnitude over general-purpose codecs.
arXiv Detail & Related papers (2020-12-20T14:47:15Z) - Two-shot Spatially-varying BRDF and Shape Estimation [89.29020624201708]
We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF.
We create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials.
Experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
arXiv Detail & Related papers (2020-04-01T12:56:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.