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openmaptiles

OpenMapTiles Vector Tile Schema

opensphere

OpenSphere

opentraj

Human Trajectory Prediction Dataset Benchmark (ACCV 2020)

opentripplanner

An open source multi-modal trip planner

optaplanner

Java Constraint Solver to solve vehicle routing, employee rostering, task assignment, conference scheduling and other planning problems.

osm-analytics

OSM Analytics lets you interactively analyze how specific OpenStreetMap features are mapped in a specific region.

osmand-tools

OsmAnd tools to generate new maps & to test OsmAnd files on PC

osmeditor4android

Vespucci is a OpenStreetMap editor for Android

osmgpxfilter

osmgpxmapmatcher

osmnx

OSMnx: Python for street networks. Retrieve, construct, analyze, and visualize street networks from OpenStreetMap.

osrm-backend

Open Source Routing Machine - C++ backend

overland-ios

📌 GPS logger for iOS devices

paper-learning

论文学习，主要研究深度学习处理遥感影像和地名识别

pg-coordtransform

基于PostgreSQL+PostGIS的火星坐标系、百度坐标系、WGS84坐标系、CGCS2000坐标系的转换函数

pixel-decoder

A machine learning python package to run deep learning with satellite imagery

pointnet2

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

postgis-spatiotemporal

Spatio-temporal trajectory functions beyond the built-in PostGIS temporal support (https://postgis.net/docs/reference.html#Temporal)

postgis_coordinate_transform

一个在postgis中结合**国情，批量对数据进行加偏到百度坐标，高德谷歌的火星坐标，或者逆向纠偏

postgis_in_action

PostGIS in Action

postgresql-postgis-timescaledb

PostgreSQL + PostGIS + TimescaleDB docker image 🐘🌎📈

ppjutils

some usefult utils like change tools and city lon and lat

predicting-poverty

Combining satellite imagery and machine learning to predict poverty

predicting-transportation-modes-of-gps-trajectories

Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv) noise removal; (v) normalization. We show that the extraction of the new point features: bearing rate, the rate of rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We also show that the noise removal task affects the performance of all the models tested. Finally, the empirical tests where we compare this work against state-of-art transportation mode prediction strategies show that our framework is competitive and outperforms most of them.

pygdaltools

pytrackdel

作业中无人机轨迹分析，得到作业面积

qgis-processing-trajectory

Trajectory tools for the QGIS Processing toolbox

rainfall-prediction-for-the-state-of-gujarat-using-deep-learning-technique

Prediction of rainfall which varies both spatially and temporally is extremely challenging. Infrared and visible spectral data from satellites have been extensively used for rainfall prediction. In this study, two deep learning methods MLP and LSTM are discussed at length for predicting precipitation at a fine spatial (10km × 10km) and temporal (hourly) resolution for the state of Gujarat. These methods are applied by using the multispectral (VIS, SWIR, MIR, WV, TIR1, TIR2) channel data such as cloud top temperature and radiance values of the INSAT-3D satellite (ISRO) as features for the model. Textural features of satellite images are incorporated by considering mean and standard deviation of each pixel’s neighbourhood. Rainfall also heavily depends on the elevation and vegetation of earth’s surface so we have used SRTM DEM and AWIFS NDVI respectively. Measurements of actual rainfall are obtained from AWS (point source stations) and TRMM (10km × 10km resolution). First dataset contains only TIR1 band temperature and AWS rainfall data for training but the second dataset includes multispectral channel data and TRMM rainfall data which brought about great improvement in results. For each data- set, a comparison between MLP and LSTM models is discussed here. We were able to classify the rainfall into nil (0mm), low ( < 2mm), medium ( > = 2mm and < 5mm) and high ( > = 5 mm) with a high accuracy. Metrics like accuracy, precision, recall and fscore have been computed to get better insights about the dataset and its corresponding outcome. Our results show that LSTM performs significantly better than MLP for any given balanced class data-sets.

raster-vision

An open source framework for deep learning on satellite and aerial imagery.

raster-vision-qgis

QGIS 3 Plugin for Raster Vision