Add Leaflet interactive map, tile generator compose, auto-sync cache, deploy docs

index.py rewritten as a continuous Leaflet map: rotated L93 tiles, stackable
visualization layers (per-layer opacity, drag-reorder persisted in
localStorage), tile info panel, live rebuild after each tile during a run.
Leaflet is vendored in assets/vendor/ so the map works fully offline;
georeferencing falls back rasterio -> pyproj -> affine so the lightweight
webapp (no GDAL) is supported.

docker-compose.worker.yml adds the tile generator service (full image + GPU)
that remote webapps call via LIDAR_GENERATION_URL, plus a one-shot process
profile. webapp.py gains LIDAR_AUTO_SYNC_SECONDS periodic cache refresh and
LIDAR_REGEN_CIDR restricting generation to the local network. run.sh
--serve-webapp now mounts ~/.ssh read-only so the rsync sync works.

docs/DEPLOY_WEBAPP.md completed for Raspberry Pi deployment: prerequisites,
git clone install, SSH key setup, first sync, update procedure and
troubleshooting.

AGENTS.md

@@ -3,6 +3,7 @@
 - install: `docker build -t lidar-lidar .` (deps baked into image)
 - build: `docker build -t lidar-lidar .`
 - build webapp légère (Raspberry Pi, déploiement 2 machines — cf. `docs/DEPLOY_WEBAPP.md`): `docker compose -f docker-compose.webapp.yml up -d --build` (image `Dockerfile.webapp`, sans PDAL/GPU)
+- build générateur de tuiles (machine de traitement): `docker compose -f docker-compose.worker.yml up -d --build` (service `worker`, API pour les webapp distantes)
 - test all: `./run.sh --test` (rebuild automatique de l'image avant les tests ; en `docker run` direct, rebuild manuellement d'abord)
 - test file: `docker run --rm lidar-lidar python3 -m pytest -v --pyargs lidar_pipeline.tests.<module>`
 - test case: `docker run --rm lidar-lidar python3 -m pytest -v --pyargs lidar_pipeline.tests.<module>::<TestClass>::<test_method>`