remove dead

Author: robmarkcole

README.md

@@ -886,8 +886,8 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [Deep-Learning-for-Solar-Panel-Recognition](https://github.com/saizk/Deep-Learning-for-Solar-Panel-Recognition) -> using both object detection with Yolov5 and Unet segmentation
 
-- [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [Dataset on kaggle](https://www.kaggle.com/datasets/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in ..  Virginia](https://github.com/bessammehenni/DeepSolar_adoption_Virginia) and [DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://github.com/gabrielkasmi/dsfrance)
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+- [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [Dataset on kaggle](https://www.kaggle.com/datasets/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in ..  Virginia]
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 - [hyperion_solar_net](https://github.com/fvergaracontesse/hyperion_solar_net) -> trained classificaton & segmentation models on RGB imagery from Google Maps
 
 - [3D-PV-Locator](https://github.com/kdmayer/3D-PV-Locator) -> Large-scale detection of rooftop-mounted photovoltaic systems in 3D
@@ -2004,7 +2004,7 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [SemiCD](https://github.com/wgcban/SemiCD) -> Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images. Achieves the performance of supervised CD even with access to as little as 10% of the annotated training data
 
-- [FCCDN_pytorch](https://github.com/chenpan0615/FCCDN_pytorch) -> FCCDN: Feature Constraint Network for VHR Image Change Detection. Uses the [LEVIR-CD](https://justchenhao.github.io/LEVIR/) building change detection dataset
+- [FCCDN_pytorch](https://github.com/chenpan0615/FCCDN_pytorch) -> FCCDN: Feature Constraint Network for VHR Image Change Detection.
 
 - [INLPG_Python](https://github.com/zcsisiyao/INLPG_Python) -> Structure Consistency based Graph for Unsupervised Change Detection with Homogeneous and Heterogeneous Remote Sensing Images
 
@@ -2249,8 +2249,6 @@ The analysis of time series observations in remote sensing data has numerous app
 
 - [spaceweather](https://github.com/sarttiso/spaceweather) -> predicting geomagnetic storms from satellite measurements of the solar wind and solar corona, uses LSTMs
 
-- [Forest_wildfire_spreading_convLSTM](https://github.com/bessammehenni/Forest_wildfire_spreading_convLSTM) -> Modeling of the spreading of forest wildfire using a neural network with ConvLSTM cells. Prediction 3-days forward
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 - [ConvTimeLSTM](https://github.com/jdiaz4302/ConvTimeLSTM) -> Extension of ConvLSTM and Time-LSTM for irregularly spaced images, appropriate for Remote Sensing
 
 - [dl-time-series](https://github.com/NexGenMap/dl-time-series) -> Deep Learning algorithms applied to characterization of Remote Sensing time-series