Annotation of datasets for deep learning applied to satellite and aerial imagery. 👉satellite-image-deep-learning.com👈 How to use this repository:if you know exactly what you are looking for (e.g. you have th
Deep learningConvolution Neural Networks (CNNs)UC-Merceed Land UseParallel computingNowadays, large amounts of high resolution remote-sensing images are acquired daily. However, the satellite image classification is requested for many applications such as modern city planning, agriculture and environmental...
Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks DenseNet40-for-HRRSISC -> DenseNet40 for remote sensing image scene classification, uses UC Merced Dataset SKAL -> Looking Closer at the Scene: Multiscale Repre...
Deep learningCloud detection in high-resolution satellite images is a critical step for many remote sensing applications, but also a challenge, as such images have limited spectral bands. The contribution of this paper is twofold: We present a dataset called CloudPeru as well as a methodology ...
We will use Classify Objects Using Deep Learning for inferencing the results. The parameters required to run the function are: in_raster:The input raster dataset to classify. The input can be a single raster or multiple rasters in a mosaic dataset, an image service, or a folder of ...
1). Then, for the training data, we further split it into 80 % and 20 % for training and validation for the deep learning model's parameterization. We also aggregated the testing data by wards, which are the local government entities in Nepal, and Kincey et al.'s dataset includes 131...
for machine learning approaches, particularly deep learning algorithms. Each whale object was represented by a point and a box (delimiting the pixels in the pansharpened image). The boxes created around the whale object were saved as one shapefile (a georeferenced file) per satellite image, as ...
Scene classification of such a huge volume of HSR-RS images is a big challenge for the efficiency of the feature learning and model training. The deep convolutional neural network (CNN), a typical deep learning model, is an efficient end-to-end deep hierarchical feature learning model that ...
This short-term network provides a rich aerosol ground measurement dataset with 8 sites in the CA Central Valley. For periods within the short-term field campaign, DISCOVER-AQ, model fit was able to achieve R2 ~ 0.8. These results were achieved using separate sub-regions of the Central SJV....
Baseline: Following a baseline deep learning method employed by Chen et al. [26], we train the feature extractor 𝑓𝜃fθ and a classifier 𝑔𝑠gs on the base dataset classes using non-episodic training. Subsequently, in the meta-testing stage, we freeze 𝑓𝜃fθ and train a new ...