Displaying 1 to 20 from 44 results

sentinelsat - Search and download Copernicus Sentinel satellite images

  •    Python

Sentinelsat makes searching, downloading and retrieving the metadata of Sentinel satellite images from the Copernicus Open Access Hub easy. and a powerful Python API.

custom-scripts - A repository of custom scripts to be used with Sentinel Hub

  •    Javascript

This repository contains a collection of custom scripts for Sentinel Hub, which can be fed to the services via the URL. You are invited to publish your own scripts - see howto.

raster-vision - deep learning for aerial/satellite imagery

  •    Python

Note: this project is under development and may be difficult to use at the moment. The overall goal of Raster Vision is to make it easy to train and run deep learning models over aerial and satellite imagery. At the moment, it includes functionality for making training data, training models, making predictions, and evaluating models for the task of object detection implemented via the Tensorflow Object Detection API. It also supports running experimental workflows using AWS Batch. The library is designed to be easy to extend to new data sources, machine learning tasks, and machine learning implementation.

get_modis - Downloading MODIS data from the USGS repository

  •    Python

This repository contains a Python script (and executable) that allows one to download MODIS data granules for different products and periods. The code is quite simple and generic, and should work with most standard Python installations.

unmixing - Interactive tools for spectral mixture analysis of multispectral raster data

  •    Python

Because this is a scientific library, there are complex dependencies that may be difficult to install. For GNU/Linux systems, particularly Ubuntu, look at install.sh for a guide on installing the system dependencies required for the Python dependencies. Many of the core packages, particularly NumPy and SciPy, have wide adoption and use. They also take a lot of time and clock cycles to compile into a virtual environment. Consequently, it is recommended that these libraries be installed globally (system-wide).

sentinel-util - A CLI for downloading, processing, and making a mosaic from Sentinel-1, -2 and -3 data

  •    Python

Sentinel Util is a command line utility to create a mosaic from Sentinel images. out The folder where the image file will be written (i.e. /path/to/your/folder).

MODIStsp - An "R" package for automatic download and preprocessing of MODIS Land Products Time Series

  •    R

MODIStsp is a “R” package devoted to automatizing the creation of time series of rasters derived from MODIS Land Products data. MODIStsp allows to perform several preprocessing steps (e.g., download, mosaicing, reprojection and resize) on MODIS data available within a given time period. Users have the ability to select which specific layers of the original MODIS HDF files they want to process. They also can select which additional Quality Indicators should be extracted from the aggregated MODIS Quality Assurance layers and, in the case of Surface Reflectance products, which Spectral Indexes should be computed from the original reflectance bands. For each output layer, outputs are saved as single-band raster filescorresponding to each available acquisition date. Virtual files allowing access to the entire time series as a single file can be also created. All processing parameters can be easily selected with a user-friendly GUI, although non-interactive execution exploiting a previously created Options File is possible. Stand-alone execution outside an “R” environment is also possible, allowing to use scheduled execution of MODIStsp to automatically update time series related to a MODIS product and extent whenever a new image is available. L. Busetto, L. Ranghetti (2016) MODIStsp: An R package for automatic preprocessing of MODIS Land Products time series, Computers & Geosciences, Volume 97, Pages 40-48, ISSN 0098-3004, http://dx.doi.org/10.1016/j.cageo.2016.08.020, URL: https://github.com/ropensci/MODIStsp.

notebooks - interactive notebooks from Planet Engineering

  •    Jupyter

In this repository, you'll find a collection of Jupyter notebooks from the software developers, data scientists, and developer advocates at Planet. These interactive, open-source (APLv2) guides are designed to help you explore Planet data, work with our APIs and tools, and learn how to extract information from our massive archive of high-cadence satellite imagery. We hope these guides will inspire you to ask interesting questions of Planet data. Need help? Find a bug? Please file an issue and we'll get back to you. Soon we hope to add notebooks from the researchers, technologists, geographers, and entrepreneurs who are already using Planet data to ask interesting and innovative questions about our changing Earth. If you're working with our imagery and have a notebook (or just an idea for a notebook) that you'd like to share, please file an issue and let us know.

pymasker - generate masks from Landsat and MODIS land product QA band

  •    Python

Pymasker is a python package to generate various masks from the Landsat Quality Assessment band and MODIS land products. The package can be shipped to your computer using pip.

retrieval-2016-remote - Multi-Label Remote Sensing Image Retrieval By Using Deep Features


Recent advances in satellite technology has led to an increased volume of remote sensing (RS) image archives, from which retrieving useful information is challenging. Therefore, one important research area in remote sensing (RS) is the content-based retrieval of RS images (CBIR). The performance of the CBIR systems relies on the capability of the RS image features in modeling the content of the images as well as the considered retrieval algorithm that assesses the similarity among the features. Using supervised classification methods in the context of CBIR by training the classifier with the already annotated images has attracted attention in RS. However, existing supervised CBIR systems in the RS literature assume that each training image is categorized by only a single label that is associated to the most significant content of the image. However, RS images usually have complex content, i.e., there are usually several regions within each image related to multiple land-cover classes. Thus, available supervised CBIR systems are not capable of accurately characterizing and exploiting the high level semantic content of RS images for retrieval problems. To overcome these problems and to effectively characterize the high-level semantic content of RS images in supervised CBIR problems, we investigate effectiveness of different deep learning architectures in the framework of multi-label remote sensing image retrieval. It is worth noting that deep learning architectures such as CNNs have recently attracted great attention in RS [1,2] due to its effective and accurate feature learning. However, according to our knowledge this is the first work that deals with adaptation of CNN models to multi-label RS image retrieval problems. This is achieved based on a two-steps strategy. In the first step, a Convolutional Neural Network (CNN) pre-trained for image classification with the ImageNet dataset is used off-the-shelf as a feature extractor. In particular, three popular architectures are explored: 1) VGG16; 2) Inception V3; and 3) ResNet50. VGG16 is a CNN characterized by 16 convolutional layers of stacked 3x3 filters, with intermediate max pooling layers and 3 fully connected layers at the end. Inception V3 is an improved version of the former GoogleNet, which contains more layers but less parameters, by removing fully connected layers and using a global average pooling from the last convolutional layer. ResNet50 is even deeper thanks to the introduction of residual layers, that allow data to flow by skipping the convolutional blocks. In the second step of our research, we modify these three off-the-shelf models by fine-tuning their parameters with a subset of RS images and their multi-label information. Experiments carried out on an archive of aerial images show that fine-tuning CNN architectures with annotated images with multi-labels significantly improve the retrieval accuracy with respect to the standard CBIR methods. We find that fine-tuning using with a multi-class approach achieves better results than considering each label as an independent class. This source code was used in the development of the master thesis of Michele Compri.

angular5-iot-dashboard - Multipurpose dashboard admin for IoT softwares, remote control, user interface

  •    TypeScript

Angular 5 Dashboard is a management dashboard for many purposes, focused on IoT, smart home, and autonomy. This project, is a fully functional app and is hosted on esam.io as an enterprise product. We are sharing many components and our workflow here inside this repository. This project can be used for Internet of things, reporting dashboard, user management, live monitoring and other other dashboard based projects for angular.

uavRst - UAV related Remote Sensing Toolbox

  •    R

The uavRmp package provides functions for rtf-UAV based autonomous mission planning. In the first place it is a simple and open source planning tool to plan autonomous terrainfollowing monitoring flights of low budget drones based on R. It provides an easy workflow for survey planning including battery-dependent task splitting, obstacle avoiding departures, and approaches of each monitoring chunks or spatial position. The uavRstanalysis toolbox package is far from being mature. You will need for most of the uavRst functions a bunch of third party software. The most comfortable way to fulfill these requirements is to install QGIS, GRASS- and SAGA-GIS. Following the excellent provided by the RQGIS team will give you a good first try to ensure a smooth working environment.

getSpatialData - An R package 📦 making it easy to query, preview, download and preprocess multiple kinds of spatial data 🛰 via R

  •    R

getSpatialData is an R package in an early development stage that ultimately aims to provide homogeneous function bundles to query, download, prepare and transform various kinds of spatial datasets from open sources, e.g. Satellite sensor data, higher-level environmental data products etc. It supports both sf and sp classes as AOI inputs (see set_aoi in available functions). Due to the early development stage, the included functions and their concepts could be removed or changed in some cases. For all public functions documentation is available. See also the list of data sources that are or will be implemented.

moveVis - This is an R package providing tools to visualize movement data by creating path animations from geo-location data

  •    R

This is an R package providing tools to visualize movement data by creating path animations from geo-location point data. The package is under ongoing development. The moveVis package is working hand in hand with the move package by using the move and moveStack class and the raster package. It is based on a ggplot2 plotting architecture and relys on the libraries ImageMagick, ffmpeg and libav. To be informed about updates, new features and the current version, visit news.movevis.org. This is the official moveVis R package repository, including beta code versions before submitted to CRAN. For operational use of moveVis, please use the current stable CRAN version of moveVis.

whitebox-tools - An advanced geospatial data analysis platform

  •    Rust

This page is related to the stand-alone command-line program and Python scripting API for geospatial analysis, WhiteboxTools. If you are instead interested in the open-source GIS, Whitebox GAT, please see this link. WhiteboxTools is an advanced geospatial data analysis platform developed by Prof. John Lindsay (webpage; jblindsay) at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. LiDAR point clouds can be interrogated (LidarInfo, LidarHistogram), segmented, tiled and joined, analyized for outliers, interpolated to rasters (DEMs, intensity images), and ground-points can be classified or filtered. WhiteboxTools is not a cartographic or spatial data visualization package; instead it is meant to serve as an analytical backend for other data visualization software, mainly GIS.

whitebox - A Python package for advanced geospatial data analysis

  •    Python

A Python package for advanced geospatial data analysis. This page is related to the whitebox Python package for geospatial analysis, which is built on a stand-alone executable command-line program called WhiteboxTools.

sentinelsat-qgis-script - QGIS thin interface to sentinelsat

  •    Python

NB: Work in progress. Not yet fully tested. You need to install sentinelsat into your QGIS Python.

phenocamr - An R Interface and Post-Processing Framework for PhenoCam Web Services

  •    R

Facilitates the retrieval and post-processing of PhenoCam time series. The post-processing of PhenoCam data includes outlier removal and the generation of data products such as phenological transition dates. If requested complementary Daymet climate data will be downloaded and merged with the PhenoCam data for modelling purposes. For a detailed overview of the assumptions made during post-processing I refer publications by Hufkens et al. (2018) and Richardson et al. (2018). Please cite the Hufkens et al. (2018) paper when using the package. A worked example is included below and in the package vignette. This will download all deciduous broadleaf (DB) PhenoCam time series for the "harvard" site at a 3-day time step into your home directory. In addition, the data is processed to estimate phenological transition dates (phenophases) and written to file. For detailed overview of all functions and worked example we reference to the R help documentation and the manuscripts below.

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