GeoAI SAM

prasanthkrishnavarman

He utilizado el nuevo Text SAM (Segment Anything Model) disponible en el Esri India Living Atlas para detectar edificios a partir de una imagen satelital con resolución de 0.3m usando el texto indicativo "Buildings." Sin embargo, el modelo solo detectó algunos edificios. ¿Cómo mejorar la precisión de detección del modelo Text SAM? Además, ¿existe alguna forma de ajustar finamente el modelo SAM para obtener mejores resultados? <\/SPAN><\/SPAN><\/P>

SwastikaDas

The issue you're encountering may be due to the varying sizes of buildings in the satellite imagery. For instance, in an urban context, there are buildings of small, medium, and large footprints.  Although deep learning models are typically trained on multiple imagery resolutions, the variation in building sizes can pose challenges. To improve detection, try manually adjusting the cell size in the environments tab of Detect Objects Using Deep Learning tool between 0.1 to 0.5. This should enhance detection across all building sizes. For more details, refer to this blog post: "https://www.esri.com/arcgis-blog/products/arcgis-pro/analytics/multiresolution-object-detection-with-text-sam/?srsltid=AfmBOoohMVcSgbSg-K_J2jtp9HCdvsALOdiEeKT02Ss-d15y2ZU_Pk6L"
Regarding your second question, yes, the SAM model can be fine-tuned. The SamLoRA model, a variation of SAM now available in ArcGIS, introduces trainable layers called Low-Rank Adaptation (LoRA) to the frozen image encoder of SAM. You can train the SamLoRA model to detect specific features of interest for more accurate results.