Add new resources for landslide detection and forest browning monitoring to README.md

Author: robmarkcole

README.md

@@ -145,6 +145,8 @@ Classification is a fundamental task in remote sensing data analysis, where the
 
 - [nshaud/DeepNetsForEO](https://github.com/nshaud/DeepNetsForEO) -> Deep networks for Earth Observation with PyTorch implementations of state-of-the-art architectures for remote sensing image classification
 
+- [sentinel-landslide-cls](https://github.com/IoannisNasios/sentinel-landslide-cls) -> Classification for Landslide Detection, using Sentinel-1 and Sentinel-2 data.
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 ## Segmentation
 
@@ -337,6 +339,8 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [agribound](https://github.com/montimaj/agribound) -> An AI-powered field boundary delineation toolkit combining satellite foundation models, embeddings, and global training data for accurate agricultural parcel/field boundary mapping.
 
+- [s2-forest-browning-monitoring](https://github.com/SamanthaBiegel/s2-forest-browning-monitoring) -> Monitoring forest browning using Sentinel-2 imagery.
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 ### Segmentation - Water, coastlines, rivers & floods
 
 - [sat-water](https://github.com/busayojee/sat-water) -> Semantic segmentation of water bodies in satellite imagery, producing pixel-wise water masks from remote sensing images using a U-Net–style deep learning pipeline (data preparation, training, inference, and evaluation).
@@ -1598,6 +1602,8 @@ Regression in remote sensing involves predicting continuous variables such as wi
 
 - [UrbanControlNet](https://github.com/kailaisun/UrbanControlNet/) -> Envisioning Global Urban Development with Satellite Imagery and Generative AI.
 
+- [Emb2Heights](https://github.com/VMarsocci/emb2heights-baselines) -> baseline for the Emb2Heights challenge - trains and runs inference for a model that predicts sub-pixel land cover percentages (Building, Vegetation, Water) and continuous structure heights (nDSM) directly from Earth Observation embeddings
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 ## Cloud detection & removal