drop numbering

README.md

@@ -3534,58 +3534,58 @@ Image quality describes the degree of accuracy with which an image can represent
 
 Training data can be hard to acquire, particularly for rare events such as change detection after disasters, or imagery of rare classes of objects. In these situations, generating synthetic training data might be the only option. This has become quite sophisticated, with 3D models being use with open source games engines such as [Unreal](https://www.unrealengine.com/en-US/).
 
-  37.1. [The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation](https://arxiv.org/ftp/arxiv/papers/2001/2001.05130.pdf) with [repo](https://github.com/timqqt/Synthinel)
+- [The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation](https://arxiv.org/ftp/arxiv/papers/2001/2001.05130.pdf) with [repo](https://github.com/timqqt/Synthinel)
 
-  37.2. [RarePlanes](https://registry.opendata.aws/rareplanes/) -> incorporates both real and synthetically generated satellite imagery including aircraft. Read the [arxiv paper](https://arxiv.org/abs/2006.02963) and checkout [this repo](https://github.com/jdc08161063/RarePlanes). Note the dataset is available through the AWS Open-Data Program for free download
+- [RarePlanes](https://registry.opendata.aws/rareplanes/) -> incorporates both real and synthetically generated satellite imagery including aircraft. Read the [arxiv paper](https://arxiv.org/abs/2006.02963) and checkout [this repo](https://github.com/jdc08161063/RarePlanes). Note the dataset is available through the AWS Open-Data Program for free download
 
-  37.3. Read [this article from NVIDIA](https://developer.nvidia.com/blog/preparing-models-for-object-detection-with-real-and-synthetic-data-and-tao-toolkit/) which discusses fine tuning a model pre-trained on synthetic data (Rareplanes) with 10% real data, then pruning the model to reduce its size, before quantizing the model to improve inference speed
+- Read [this article from NVIDIA](https://developer.nvidia.com/blog/preparing-models-for-object-detection-with-real-and-synthetic-data-and-tao-toolkit/) which discusses fine tuning a model pre-trained on synthetic data (Rareplanes) with 10% real data, then pruning the model to reduce its size, before quantizing the model to improve inference speed
 
-  37.4. [Combining Synthetic Data with Real Data to Improve Detection Results in Satellite Imagery](https://one-view.ai/combining-synthetic-data-with-real-data-to-improve-detection-results-in-satellite-imagery-case-study/)
+- [Combining Synthetic Data with Real Data to Improve Detection Results in Satellite Imagery](https://one-view.ai/combining-synthetic-data-with-real-data-to-improve-detection-results-in-satellite-imagery-case-study/)
 
-  37.5. [BlenderGIS](https://github.com/domlysz/BlenderGIS) could be used for synthetic data generation
+- [BlenderGIS](https://github.com/domlysz/BlenderGIS) could be used for synthetic data generation
 
-  37.6. [bifrost.ai](https://www.bifrost.ai/) -> simulated data service with geospatial output data formats
+- [bifrost.ai](https://www.bifrost.ai/) -> simulated data service with geospatial output data formats
 
-  37.7. [oktal-se](https://www.oktal-se.fr/deep-learning/) -> software for generating simulated data across a wide range of bands including optical and SAR
+- [oktal-se](https://www.oktal-se.fr/deep-learning/) -> software for generating simulated data across a wide range of bands including optical and SAR
 
-  37.8. [The Nuances of Extracting Utility from Synthetic Data](https://www.iqt.org/synthesizing-robustness-yoltv4-results-part-1/) -> We find that strategically augmenting the real dataset is nearly as effective as adding synthetic data in the quest to improve the detection or rare object classes, and that fully extracting the utility of synthetic data is a nuanced process
+- [The Nuances of Extracting Utility from Synthetic Data](https://www.iqt.org/synthesizing-robustness-yoltv4-results-part-1/) -> We find that strategically augmenting the real dataset is nearly as effective as adding synthetic data in the quest to improve the detection or rare object classes, and that fully extracting the utility of synthetic data is a nuanced process
 
-  37.9. [Synthesizing Robustness](https://www.iqt.org/synthesizing-robustness/) -> explores how to best leverage and enhance synthetic data
+- [Synthesizing Robustness](https://www.iqt.org/synthesizing-robustness/) -> explores how to best leverage and enhance synthetic data
 
-  37.10. [rendered.ai](https://rendered.ai/) -> The Platform as a Service for Creating Synthetic Data
+- [rendered.ai](https://rendered.ai/) -> The Platform as a Service for Creating Synthetic Data
 
-  37.11. [synthetic_xview_airplanes](https://github.com/yangxu351/synthetic_xview_airplanes) -> creation of airplanes synthetic dataset using ArcGIS CityEngine
+- [synthetic_xview_airplanes](https://github.com/yangxu351/synthetic_xview_airplanes) -> creation of airplanes synthetic dataset using ArcGIS CityEngine
 
-  37.12. [Combining Synthetic Data with Real Data to Improve Detection Results in Satellite Imagery: Case Study](https://one-view.ai/combining-synthetic-data-with-real-data-to-improve-detection-results-in-satellite-imagery-case-study/)
+- [Combining Synthetic Data with Real Data to Improve Detection Results in Satellite Imagery: Case Study](https://one-view.ai/combining-synthetic-data-with-real-data-to-improve-detection-results-in-satellite-imagery-case-study/)
 
-  37.13. [SynImageAnalysis](https://github.com/FlorenceJiang/SynImageAnalysis) -> comparing synthetic and real sattlelite images in the latent feature space (embeddings)
+- [SynImageAnalysis](https://github.com/FlorenceJiang/SynImageAnalysis) -> comparing synthetic and real sattlelite images in the latent feature space (embeddings)
 
-  37.14. [Import OpenStreetMap data into Unreal Engine 4](https://github.com/ue4plugins/StreetMap)
+- [Import OpenStreetMap data into Unreal Engine 4](https://github.com/ue4plugins/StreetMap)
 
-  37.15. [deepfake-satellite-images](https://github.com/RijulGupta-DM/deepfake-satellite-images) -> dataset that includes over 1M images of synthetic aerial images
+- [deepfake-satellite-images](https://github.com/RijulGupta-DM/deepfake-satellite-images) -> dataset that includes over 1M images of synthetic aerial images
 
-  37.16. [synthetic-disaster](https://github.com/JakeForsey/synthetic-disaster) -> Generate synthetic satellite images of natural disasters using deep neural networks
+- [synthetic-disaster](https://github.com/JakeForsey/synthetic-disaster) -> Generate synthetic satellite images of natural disasters using deep neural networks
 
-  37.17. [STPLS3D](https://github.com/meidachen/STPLS3D) -> A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
+- [STPLS3D](https://github.com/meidachen/STPLS3D) -> A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
 
-  37.18. [LESS](https://github.com/jianboqi/lessrt) -> LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes
+- [LESS](https://github.com/jianboqi/lessrt) -> LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes
 
-  37.19. [Synthesizing Robustness: Dataset Size Requirements and Geographic Insights](https://avanetten.medium.com/synthesizing-robustness-dataset-size-requirements-and-geographic-insights-a687192e8004) -> Medium article, concludes that synthetic data is most beneficial to the rarest object classes and that extracting utility from synthetic data often takes significant effort and creativity
+- [Synthesizing Robustness: Dataset Size Requirements and Geographic Insights](https://avanetten.medium.com/synthesizing-robustness-dataset-size-requirements-and-geographic-insights-a687192e8004) -> Medium article, concludes that synthetic data is most beneficial to the rarest object classes and that extracting utility from synthetic data often takes significant effort and creativity
 
-  37.20. [rs_img_synth](https://github.com/gbaier/rs_img_synth) -> Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data
+- [rs_img_synth](https://github.com/gbaier/rs_img_synth) -> Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data
 
-  37.21. [OnlyPlanes](https://github.com/naivelogic/OnlyPlanes) -> dataset and pretrained models for the paper: OnlyPlanes - Incrementally Tuning Synthetic Training Datasets for Satellite Object Detection
+- [OnlyPlanes](https://github.com/naivelogic/OnlyPlanes) -> dataset and pretrained models for the paper: OnlyPlanes - Incrementally Tuning Synthetic Training Datasets for Satellite Object Detection
 
 #
 ## 38. Large vision & language models (LLMs & LVMs)
 
-  38.1. [awesome-remote-sensing-vision-language-models](https://github.com/lzw-lzw/awesome-remote-sensing-vision-language-models)
-  
-  38.2. [Remote-Sensing-ChatGPT](https://github.com/HaonanGuo/Remote-Sensing-ChatGPT) -> an open source tool for solving remote sensing tasks with ChatGPT in an interactive way.
+- [awesome-remote-sensing-vision-language-models](https://github.com/lzw-lzw/awesome-remote-sensing-vision-language-models)
+
+- [Remote-Sensing-ChatGPT](https://github.com/HaonanGuo/Remote-Sensing-ChatGPT) -> an open source tool for solving remote sensing tasks with ChatGPT in an interactive way.
 
-  38.3. [ChangeCLIP](https://github.com/dyzy41/ChangeCLIP) -> ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning
+- [ChangeCLIP](https://github.com/dyzy41/ChangeCLIP) -> ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning
 
-  38.4. [SkyEyeGPT](https://github.com/ZhanYang-nwpu/SkyEyeGPT) -> SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model
+- [SkyEyeGPT](https://github.com/ZhanYang-nwpu/SkyEyeGPT) -> SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model
 
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