various

Author: robmarkcole

README.md

@@ -331,6 +331,8 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [paddy_identification](https://github.com/kayathri4/paddy_identification) -> Paddy Field Instance Segmentation using Multi-Temporal SAR Time Series
 
+- [CropSight](https://github.com/rssiuiuc/CropSight) -> towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and PlanetScope satellite imagery
+
 ### 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).
@@ -737,6 +739,10 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [Building Segmentation on LR-HR-SR Satellite Imagery](https://github.com/ESAOpenSR/Segmentation-Models-Benchmark) -> perform building delineation on different types of satellite imagery: Low-Resolution (LR), High-Resolution (HR), and Super-Resolution (SR). The goal is to compare the performance of segmentation models across these varying resolutions.
 
+- [UrbanGraphSAGE](https://github.com/OMUZ9924/UrbanGraphSAGE) -> Graph Neural Network (GraphSAGE) for urban building footprint extraction from Sentinel-2 satellite imagery
+
+- [terratorch-building-segmentation](https://github.com/OMUZ9924/terratorch-building-segmentation) -> Fine-tuning Geospatial Foundation Models (Prithvi, TerraMind) for building footprint segmentation from Sentinel-2 using TerraTorch — Algiers case study
+
 ### Segmentation - Solar panels
 
 - [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
@@ -1582,6 +1588,8 @@ Regression in remote sensing involves predicting continuous variables such as wi
 
 - [ECHOSAT](https://github.com/AI4Forest/ECHOSAT) -> Estimating Canopy Height Over Space And Time, uses a Swin Video UNet architecture that processes multi-sensor satellite data.
 
+- [Popcorn](https://popcorn-population.github.io/) -> High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2, with follow up work [Bourbon](https://github.com/nandometzger/bourbon)
+
 #
 ## Cloud detection & removal
 
@@ -2201,6 +2209,10 @@ Crop yield is a crucial metric in agriculture, as it determines the productivity
 
 - [Bayesian-posterior-based-EnKF](https://github.com/paperoses/Bayesian-posterior-based-EnKF) -> The improved winter wheat yield estimation by assimilating GLASS LAI into a crop growth model with the proposed Bayesian posterior-based ensemble Kalman filter.
 
+- [PBCNN](https://github.com/rssiuiuc/PBCNN) -> A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis.
+
+- [imbalance_deep_regression_yield_forecasting](https://github.com/plant-ai-biophysics-lab/imbalance_deep_regression_yield_forecasting) -> Predicting Crop Yield Lows Through Highs via Binned Deep Imbalanced Regression
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 #
 ## Wealth and economic activity
 
@@ -2718,6 +2730,8 @@ Data fusion is a technique for combining information from different sources such
 
 - [AnytimeFormer](https://github.com/tangkai-RS/AnytimeFormer) -> Fusing irregular and asynchronous SAR-optical time series to reconstruct reflectance at any given time
 
+- [Rose](https://github.com/bailubin/Rose) -> Integrating remote sensing with OpenStreetMap data for comprehensive scene understanding through multi-modal self-supervised learning
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 ## Generative networks
 
@@ -2902,6 +2916,8 @@ Autoencoders are a type of neural network that aim to simplify the representatio
 
 - [RS-Embed](https://github.com/cybergis/rs-embed) -> A single line of code to get embeddings from Any Remote Sensing Foundation Model (RSFM) for Any Place and Any Time
 
+- [SatelliteBench](https://github.com/mitcriticaldatacolombia/SatelliteBench) -> a data fusion framework that combines satellite images and tabular data for dengue prediction and socioeconomic analysis
+
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 ## Anomaly detection
 Anomaly detection refers to the process of identifying unusual patterns or outliers in satellite or aerial images that do not conform to expected norms. This is crucial in applications such as environmental monitoring, defense surveillance, and urban planning. Machine learning algorithms, particularly unsupervised learning methods, are used to analyze vast amounts of remote sensing data efficiently. These algorithms learn the typical patterns and variations in the data, allowing them to flag anomalies such as unexpected land cover changes, illegal deforestation, or unusual maritime activities. The detection of these anomalies can provide valuable insights for timely decision-making and intervention in various fields.
@@ -3775,5 +3791,9 @@ Explainable AI (XAI) is a field of artificial intelligence that focuses on devel
 
 - [Wildfire Forecasting](https://asucicilab.github.io/wildfire-forecasting/) -> Adapting Video Foundation Models for Spatiotemporal Wildfire Forecasting via Cross-Modal Progressive Fine-Tuning
 
+## Geospatial Agents
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+- [OpenEarthAgent](https://github.com/mbzuai-oryx/OpenEarthAgent) -> a unified framework for tool-augmented geospatial agents
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