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-baseline

高鲁棒性要求下的领域事件检测任务baseline，转化为ner的形式做任务

amsr-few-shot

Adapting Multi-source Representations for Cross-Domain Few-shot Learning (CD-FSL)

awesome-self-supervised-learning

A curated list of awesome self-supervised methods

building-height-deu

D. Frantz, F. Schug, A. Okujeni, C. Navacchi, W. Wagner, S. van der Linden, and P. Hostert (2021): National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment 252, 112128. https://doi.org/10.1016/j.rse.2020.112128

chinesetoimg

Python3生成汉字字库并转换为图片

cmir-net-a-deep-learning-based-model-for-cross-modal-retrieval-in-remote-sensing

We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multispectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech-based label annotations. These multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the increasing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network-based architecture that is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multispectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases.

cpu-gpu-benchmark

CPU-Benchmark CPU单核多核跑分图

deeplearningnote

this is all of my code and data with my deep learning note

demo_dhcnn_for_tgrs2021

A novel deep hashing method (DHCNN) for remote sensing image retrieval and classification, which was pulished in IEEE Trans. Geosci. Remote Sens., 2021.

fast-reid

SOTA Re-identification Methods and Toolbox

geodjango-vue-leaflet-demo

The project shows how we can build an API using Django/GeoDjango, the Django Rest framework, Django-rest-framework-gis, and output data (from a PostgreSQL database) in a format that is GeoJSON compatible. The API is used in a Vue application which displays data randomly on a web map (Leaflet) using polling.

hanlp

中文分词 词性标注 命名实体识别 依存句法分析 语义依存分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理

helloworld

image_match

An image retrieval and matching method based on color histogram.

leeml-notes

李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes

lsh_pytorch

Source code for paper "Similarity Search in High Dimensions via Hashing" on VLDH-1999

map-marker-openlayers

OpenLayers map marker popup. Map delivery area. Find location from address, geolocation. Multiple markers with html popups. Import and export polygon with openlayers map..

ml4a-guides

practical guides, tutorials, and code samples for ml4a

mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

multi-source-domain-adapation

openselfsup

Self-Supervised Learning Toolbox and Benchmark

paddleclas

A treasure chest for visual recognition powered by PaddlePaddle

pv-plant-dataset-of-china

PV plant vector dataset of China for year 2023.

pv_scientificdata_classification_code

The GEE code for PV power stations classification based on Sentinel-2 imagery and DEM data. The code is written in JavaScript, including all the mentioned steps in the paper, A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020, including feature calculation, random forest training, etc.

pyretri

Open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥

sat_to_map

Learning mappings to generate city maps images from corresponding satellite images.

ssc_csharp

C# version of Sound Shape Code(SSC)

test

traditional-feature-extraction-methods

Feature Extraction is an integral step for Image Processing jobs. This repository contains the python codes for Traditonal Feature Extraction Methods from an image dataset, namely Gabor, Haralick, Tamura, GLCM and GLRLM.

visualtransformers

A Pytorch Implementation of the following paper "Visual Transformers: Token-based Image Representation and Processing for Computer Vision"