Showing 67 of total 67 results (show query)

ropensci

stplanr:Sustainable Transport Planning

Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) <doi:10.5198/jtlu.2016.862>, but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) <doi:10.1016/j.jtrangeo.2017.08.012> and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) <doi:10.1016/j.enconman.2023.117337>. The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as <https://cyclestreets.net/> (Desjardins et al. 2021) <doi:10.1007/s11116-021-10197-1>; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779> and the 'OD jittering' method described in Lovelace et al. (2022) <doi:10.32866/001c.33873>. Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053>, and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) <doi:10.1007/s10109-020-00342-2>.

Maintained by Robin Lovelace. Last updated 7 months ago.

cyclecyclingdesire-linesorigin-destinationpeer-reviewedpubic-transportroute-networkroutesroutingspatialtransporttransport-planningtransportationwalking

427 stars 12.31 score 684 scripts 3 dependents

e-sensing

sits:Satellite Image Time Series Analysis for Earth Observation Data Cubes

An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Includes methods to reduce training samples imbalance proposed by Chawla et al (2002) <doi:10.1613/jair.953>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference as described by Camara et al (2024) <doi:10.3390/rs16234572>, and methods for active learning and uncertainty assessment. Supports region-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Maintained by Gilberto Camara. Last updated 2 months ago.

big-earth-datacbersearth-observationeo-datacubesgeospatialimage-time-seriesland-cover-classificationlandsatplanetary-computerr-spatialremote-sensingrspatialsatellite-image-time-seriessatellite-imagerysentinel-2stac-apistac-catalogcpp

494 stars 9.50 score 384 scripts

r-spatial

mapedit:Interactive Editing of Spatial Data in R

Suite of interactive functions and helpers for selecting and editing geospatial data.

Maintained by Tim Appelhans. Last updated 3 years ago.

218 stars 8.20 score 410 scripts 1 dependents

poissonconsulting

fwatlasbc:Freshwater Atlas British Columbia

What the package does (one paragraph).

Maintained by Joe Thorley. Last updated 2 months ago.

bcfwapgfwapgr

9 stars 3.73 score 5 scripts

ropensci

ReLTER:An Interface for the eLTER Community

ReLTER provides access to DEIMS-SDR (https://deims.org/), and allows interaction with data and software implemented by eLTER Research Infrastructure (RI) thus improving data sharing among European LTER projects. ReLTER uses the R language to access and interact with the DEIMS-SDR archive of information shared by the Long Term Ecological Research (LTER) network. This package grew within eLTER H2020 as a major project that will help advance the development of European Long-Term Ecosystem Research Infrastructures (eLTER RI - https://elter-ri.eu). The ReLTER package functions in particular allow to: - retrieve the information about entities (e.g. sites, datasets, and activities) shared by DEIMS-SDR (see e.g. get_site_info function); - interact with the [ODSEurope](maps.opendatascience.eu) starting with the dataset shared by [DEIMS-SDR](https://deims.org/) (see e.g. [get_site_ODS](https://docs.ropensci.org/ReLTER/reference/get_site_ODS.html) function); - use the eLTER site informations to download and crop geospatial data from other platforms (see e.g. get_site_ODS function); - improve the quality of the dataset (see e.g. get_id_worms). Functions currently implemented are derived from discussions of the needs among the eLTER users community. The ReLTER package will continue to follow the progress of eLTER-RI and evolve, adding new tools and improvements as required.

Maintained by Alessandro Oggioni. Last updated 1 years ago.

biodiversity-informaticsdata-scienceecologyelterresearch-infrastructure

12 stars 3.38 score 4 scripts

nenuial

geographer:Geography Vizualisations

Provides function and objects to establish vizualisations for my Geography lessons.

Maintained by Pascal Burkhard. Last updated 1 months ago.

2 stars 3.08 score

carlosyanez

aussiemaps:Maps of Australia

Granular maps of Australia using ABS boundaries from 2006-2021.

Maintained by Carlos Yáñez Santibáñez. Last updated 2 years ago.

australiamapsr-spatialsf-objects

1 stars 3.06 score 23 scripts

nowosad

geostatbook:Geostatystyka w R

Materialy do skryptu Geostatystyka w R.

Maintained by Jakub Nowosad. Last updated 3 years ago.

6 stars 2.88 score 25 scripts