flsmedia's repositories
laspy
Laspy is a pythonic interface for reading/modifying/creating .LAS LIDAR files matching specification 1.0-1.4.
Baidu-spider
爬取百度地图poi数据
mmdetection
OpenMMLab Detection Toolbox and Benchmark
LasConverter
Converts LAS Files (LIDAR data) to DSM/DEMs with an optional shapefile / kml file to clip to.
building-extraction
Remote sensing image building extraction and optimization, using TF2
DiResNet
Codes for the 'Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images'
arcgis-osm-editor
ArcGIS Editor for OpenStreetMap is a toolset for GIS users to access and contribute to OpenStreetMap through their Desktop or Server environment.
randomforestbtc
Uses a random forest (RF) regression model to predict bitcoin prices 3 days ahead. Trained on data from 2010-2019.
poi
百度以及高德地图POI数据和POI边界坐标数据(目前仅限百度)爬取代码，采用Python编写
Regression-stacked-SVM_RandomForest
This is my implementation of a stacked regressor using optimized SVM and random Forest using Optuna.The actual inputs of the combined regressor is a latent representation of 220 inputs compressed into 5 ,extracted using an auto-encoder implemented under Keras
HRSID
HRSID: high resolution sar images dataset for ship detection, semantic segmentation, and instance segmentation tasks.
SPP
Realizing pseudo range single point positioning and pseudo range differential positioning.
xG-MATLAB-Model
A rudimentary expected goals (xG) model using RandomForest Machine Learning model in MATLAB
Random-Forest-Regression-2
Regression-Random Forest
airbnb_price_prediction
Price prediction of airbnb offers using random forest regression.
DEM_compare
Validation of high resolution satellite derived DEM and 'ground-truth' LIDAR
LiDARForestryHeight
Plugin generates heights raster maps from LiDAR classified point clouds (las and laz formats)
Health-outcome-prediction
Build models using tweets data and demographic data, including pca, KNN, linear regression, lasso regression, svm, random forests, bagging, boosting etc.
FCNs-for-road-extraction-keras
Road extraction of high-resolution remote sensing images based on various semantic segmentation networks
Gradient-Boosting-Regression
I used gradient boosting regression and random forest regression to predict real estate prices for the kaggle task titled "House Prices: Advanced Regression Techniques"
Predicting_real_estate_prices_using_scikit-learn
Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
LidarProcessing
Process lidar data to create canopy models and DEMs
road_extraction_remote_sensing
Road Extraction based on U-Net architecture (CVPR2018 DeepGlobe Challenge submission)
soilscape_upscaling
Code for upscaling soil moisture with Random Forests regression
svr
Support Vector Regression
Building-A-Nets
Building-a-nets: robust building extraction from high-resolution remote sensing images with adversarial networks
Random-Forest-Regression-1
Random Forest Regression