diff --git a/rastervision_core/rastervision/core/box.py b/rastervision_core/rastervision/core/box.py
index fe994a5d8..c2f959778 100644
--- a/rastervision_core/rastervision/core/box.py
+++ b/rastervision_core/rastervision/core/box.py
@@ -1,6 +1,6 @@
 from typing import (TYPE_CHECKING, Callable, Dict, List, Literal, Optional,
                     Sequence, Tuple, Union)
-from pydantic import PositiveInt as PosInt, conint
+from pydantic import NonNegativeInt as NonNegInt, PositiveInt as PosInt
 import math
 import random
 
@@ -11,8 +11,6 @@
 
 from rastervision.pipeline.utils import repr_with_args
 
-NonNegInt = conint(ge=0)
-
 if TYPE_CHECKING:
     from shapely.geometry import MultiPolygon
 
diff --git a/rastervision_core/rastervision/core/data/class_config.py b/rastervision_core/rastervision/core/data/class_config.py
index 3f63de66d..3594a0de5 100644
--- a/rastervision_core/rastervision/core/data/class_config.py
+++ b/rastervision_core/rastervision/core/data/class_config.py
@@ -1,7 +1,7 @@
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Self, Tuple, Union
 
 from rastervision.pipeline.config import (Config, register_config, ConfigError,
-                                          Field, validator)
+                                          Field, model_validator)
 from rastervision.core.data.utils import color_to_triple, normalize_color
 
 DEFAULT_NULL_CLASS_NAME = 'null'
@@ -32,42 +32,43 @@ class ClassConfig(Config):
         'Config is part of a SemanticSegmentationConfig, a null class will be '
         'added automatically.')
 
-    @validator('colors', always=True)
-    def validate_colors(cls, v: Optional[List[Union[str, Tuple]]],
-                        values: dict) -> Optional[List[Union[str, Tuple]]]:
+    @model_validator(mode='after')
+    def validate_colors(self) -> Self:
         """Compare length w/ names. Also auto-generate if not specified."""
-        class_names = values['names']
-        class_colors = v
-        if class_colors is None:
-            class_colors = [color_to_triple() for _ in class_names]
-        elif len(class_names) != len(class_colors):
-            raise ConfigError(f'len(class_names) ({len(class_names)}) != '
-                              f'len(class_colors) ({len(class_colors)})\n'
-                              f'class_names: {class_names}\n'
-                              f'class_colors: {class_colors}')
-        return class_colors
-
-    @validator('null_class', always=True)
-    def validate_null_class(cls, v: Optional[str],
-                            values: dict) -> Optional[str]:
+        names = self.names
+        colors = self.colors
+        if colors is None:
+            self.colors = [color_to_triple() for _ in names]
+        elif len(names) != len(colors):
+            raise ConfigError(f'len(class_names) ({len(names)}) != '
+                              f'len(class_colors) ({len(colors)})\n'
+                              f'class_names: {names}\n'
+                              f'class_colors: {colors}')
+        return self
+
+    @model_validator(mode='after')
+    def validate_null_class(self) -> Self:
         """Check if in names. If 'null' in names, use it as null class."""
-        names = values['names']
-        if v is None:
+        names = self.names
+        null_class = self.null_class
+        if null_class is None:
             if DEFAULT_NULL_CLASS_NAME in names:
-                v = DEFAULT_NULL_CLASS_NAME
+                self.null_class = DEFAULT_NULL_CLASS_NAME
         else:
-            if v not in names:
+            if null_class not in names:
                 raise ConfigError(
-                    f'The null_class, "{v}", must be in list of class names.')
+                    f'The null_class, "{null_class}", must be in list of '
+                    'class names.')
 
             # edge case
             default_null_class_in_names = (DEFAULT_NULL_CLASS_NAME in names)
-            null_class_neq_default = (v != DEFAULT_NULL_CLASS_NAME)
+            null_class_neq_default = (null_class != DEFAULT_NULL_CLASS_NAME)
             if default_null_class_in_names and null_class_neq_default:
                 raise ConfigError(
                     f'"{DEFAULT_NULL_CLASS_NAME}" is in names but the '
-                    f'specified null_class is something else ("{v}").')
-        return v
+                    'specified null_class is something else '
+                    f'("{null_class}").')
+        return self
 
     def get_class_id(self, name: str) -> int:
         return self.names.index(name)
diff --git a/rastervision_core/rastervision/core/data/label_source/chip_classification_label_source_config.py b/rastervision_core/rastervision/core/data/label_source/chip_classification_label_source_config.py
index faf188185..a1ceaed1c 100644
--- a/rastervision_core/rastervision/core/data/label_source/chip_classification_label_source_config.py
+++ b/rastervision_core/rastervision/core/data/label_source/chip_classification_label_source_config.py
@@ -1,10 +1,10 @@
-from typing import Optional
+from typing import Optional, Self
 
 from rastervision.core.data.vector_source import (VectorSourceConfig)
 from rastervision.core.data.label_source import (LabelSourceConfig,
                                                  ChipClassificationLabelSource)
 from rastervision.pipeline.config import (ConfigError, register_config, Field,
-                                          validator, root_validator)
+                                          field_validator, model_validator)
 from rastervision.core.data.vector_transformer import (
     ClassInferenceTransformerConfig, BufferTransformerConfig)
 
@@ -62,7 +62,8 @@ class ChipClassificationLabelSourceConfig(LabelSourceConfig):
         description='If True, labels will not be populated automatically '
         'during initialization of the label source.')
 
-    @validator('vector_source')
+    @field_validator('vector_source')
+    @classmethod
     def ensure_required_transformers(
             cls, v: VectorSourceConfig) -> VectorSourceConfig:
         """Add class-inference and buffer transformers if absent."""
@@ -84,14 +85,14 @@ def ensure_required_transformers(
 
         return v
 
-    @root_validator(skip_on_failure=True)
-    def ensure_bg_class_id_if_inferring(cls, values: dict) -> dict:
-        infer_cells = values.get('infer_cells')
-        has_bg_class_id = values.get('background_class_id') is not None
+    @model_validator(mode='after')
+    def ensure_bg_class_id_if_inferring(self) -> Self:
+        infer_cells = self.infer_cells
+        has_bg_class_id = self.background_class_id is not None
         if infer_cells and not has_bg_class_id:
             raise ConfigError(
                 'background_class_id is required if infer_cells=True.')
-        return values
+        return self
 
     def build(self, class_config, crs_transformer, bbox=None,
               tmp_dir=None) -> ChipClassificationLabelSource:
diff --git a/rastervision_core/rastervision/core/data/label_source/object_detection_label_source_config.py b/rastervision_core/rastervision/core/data/label_source/object_detection_label_source_config.py
index dfe7551be..10347c254 100644
--- a/rastervision_core/rastervision/core/data/label_source/object_detection_label_source_config.py
+++ b/rastervision_core/rastervision/core/data/label_source/object_detection_label_source_config.py
@@ -3,7 +3,7 @@
 from rastervision.core.data.vector_source import VectorSourceConfig
 from rastervision.core.data.vector_transformer import (
     ClassInferenceTransformerConfig, BufferTransformerConfig)
-from rastervision.pipeline.config import register_config, validator
+from rastervision.pipeline.config import register_config, field_validator
 
 
 @register_config('object_detection_label_source')
@@ -12,7 +12,8 @@ class ObjectDetectionLabelSourceConfig(LabelSourceConfig):
 
     vector_source: VectorSourceConfig
 
-    @validator('vector_source')
+    @field_validator('vector_source')
+    @classmethod
     def ensure_required_transformers(
             cls, v: VectorSourceConfig) -> VectorSourceConfig:
         """Add class-inference and buffer transformers if absent."""
diff --git a/rastervision_core/rastervision/core/data/raster_source/multi_raster_source.py b/rastervision_core/rastervision/core/data/raster_source/multi_raster_source.py
index f617e9c78..d86627d10 100644
--- a/rastervision_core/rastervision/core/data/raster_source/multi_raster_source.py
+++ b/rastervision_core/rastervision/core/data/raster_source/multi_raster_source.py
@@ -1,6 +1,6 @@
 from typing import TYPE_CHECKING, Optional, Sequence, Self, Tuple
-from pydantic import conint
 
+from pydantic import NonNegativeInt as NonNegInt
 import numpy as np
 from pystac import Item
 
@@ -18,29 +18,28 @@ class MultiRasterSource(RasterSource):
 
     def __init__(self,
                  raster_sources: Sequence[RasterSource],
-                 primary_source_idx: conint(ge=0) = 0,
+                 primary_source_idx: NonNegInt = 0,
                  force_same_dtype: bool = False,
-                 channel_order: Optional[Sequence[conint(ge=0)]] = None,
+                 channel_order: Sequence[NonNegInt] | None = None,
                  raster_transformers: Sequence = [],
-                 bbox: Optional[Box] = None):
+                 bbox: Box | None = None):
         """Constructor.
 
         Args:
-            raster_sources (Sequence[RasterSource]): Sequence of RasterSources.
+            raster_sources: Sequence of RasterSources.
             primary_source_idx (0 <= int < len(raster_sources)): Index of the
                 raster source whose CRS, dtype, and other attributes will
                 override those of the other raster sources.
-            force_same_dtype (bool): If true, force all sub-chips to have the
+            force_same_dtype: If true, force all sub-chips to have the
                 same dtype as the primary_source_idx-th sub-chip. No careful
                 conversion is done, just a quick cast. Use with caution.
-            channel_order (Sequence[conint(ge=0)], optional): Channel ordering
-                that will be used by .get_chip(). Defaults to None.
-            raster_transformers (Sequence, optional): Sequence of transformers.
-                Defaults to [].
-            bbox (Optional[Box], optional): User-specified crop of the extent.
-                If given, the primary raster source's bbox is set to this.
-                If None, the full extent available in the source file of the
-                primary raster source is used.
+            channel_order: Channel ordering that will be used by
+                :meth:`MultiRasterSource.get_chip()`. Defaults to ``None``.
+            raster_transformers: List of transformers. Defaults to ``[]``.
+            bbox: User-specified crop of the extent. If specified, the primary
+                raster source's bbox is set to this. If ``None``, the full
+                extent available in the source file of the primary raster
+                source is used.
         """
         num_channels_raw = sum(rs.num_channels for rs in raster_sources)
         if not channel_order:
@@ -78,7 +77,7 @@ def from_stac(
             cls,
             item: Item,
             assets: list[str] | None,
-            primary_source_idx: conint(ge=0) = 0,
+            primary_source_idx: NonNegInt = 0,
             raster_transformers: list['RasterTransformer'] = [],
             force_same_dtype: bool = False,
             channel_order: Sequence[int] | None = None,
diff --git a/rastervision_core/rastervision/core/data/raster_source/multi_raster_source_config.py b/rastervision_core/rastervision/core/data/raster_source/multi_raster_source_config.py
index f223e7c87..19f31c40e 100644
--- a/rastervision_core/rastervision/core/data/raster_source/multi_raster_source_config.py
+++ b/rastervision_core/rastervision/core/data/raster_source/multi_raster_source_config.py
@@ -1,7 +1,10 @@
-from typing import Optional
-from pydantic import conint, conlist
+from typing import List, Optional, Self
 
-from rastervision.pipeline.config import (Field, register_config, validator)
+from typing_extensions import Annotated
+from pydantic import NonNegativeInt as NonNegInt
+
+from rastervision.pipeline.config import (Field, register_config,
+                                          model_validator)
 from rastervision.core.box import Box
 from rastervision.core.data.raster_source import (RasterSourceConfig,
                                                   MultiRasterSource)
@@ -25,10 +28,10 @@ class MultiRasterSourceConfig(RasterSourceConfig):
     Or :class:`.TemporalMultiRasterSource`, if ``temporal=True``.
     """
 
-    raster_sources: conlist(
-        RasterSourceConfig, min_items=1) = Field(
+    raster_sources: Annotated[List[
+        RasterSourceConfig], Field(min_length=1)] = Field(
             ..., description='List of RasterSourceConfig to combine.')
-    primary_source_idx: conint(ge=0) = Field(
+    primary_source_idx: NonNegInt = Field(
         0,
         description=
         'Index of the raster source whose CRS, dtype, and other attributes '
@@ -42,21 +45,21 @@ class MultiRasterSourceConfig(RasterSourceConfig):
         description='Stack images from sub raster sources into a time-series '
         'of shape (T, H, W, C) instead of concatenating bands.')
 
-    @validator('primary_source_idx')
-    def validate_primary_source_idx(cls, v: int, values: dict):
-        raster_sources = values.get('raster_sources', [])
-        if not (0 <= v < len(raster_sources)):
+    @model_validator(mode='after')
+    def validate_primary_source_idx(self) -> Self:
+        primary_source_idx = self.primary_source_idx
+        raster_sources = self.raster_sources
+        if not (0 <= primary_source_idx < len(raster_sources)):
             raise IndexError('primary_source_idx must be in range '
                              '[0, len(raster_sources)].')
-        return v
+        return self
 
-    @validator('temporal')
-    def validate_temporal(cls, v: int, values: dict):
-        channel_order = values.get('channel_order')
-        if v and channel_order is not None:
+    @model_validator(mode='after')
+    def validate_temporal(self) -> Self:
+        if self.temporal and self.channel_order is not None:
             raise ValueError(
                 'Setting channel_order is not allowed if temporal=True.')
-        return v
+        return self
 
     def build(self,
               tmp_dir: Optional[str] = None,
diff --git a/rastervision_core/rastervision/core/data/raster_source/rasterized_source_config.py b/rastervision_core/rastervision/core/data/raster_source/rasterized_source_config.py
index ff6449c15..efc30bd35 100644
--- a/rastervision_core/rastervision/core/data/raster_source/rasterized_source_config.py
+++ b/rastervision_core/rastervision/core/data/raster_source/rasterized_source_config.py
@@ -5,7 +5,7 @@
 from rastervision.core.data.vector_transformer import (
     ClassInferenceTransformerConfig, BufferTransformerConfig)
 from rastervision.pipeline.config import (register_config, Config, Field,
-                                          validator)
+                                          field_validator)
 
 if TYPE_CHECKING:
     from rastervision.core.box import Box
@@ -35,7 +35,8 @@ class RasterizedSourceConfig(Config):
     vector_source: VectorSourceConfig
     rasterizer_config: RasterizerConfig
 
-    @validator('vector_source')
+    @field_validator('vector_source')
+    @classmethod
     def ensure_required_transformers(
             cls, v: VectorSourceConfig) -> VectorSourceConfig:
         """Add class-inference and buffer transformers if absent."""
diff --git a/rastervision_core/rastervision/core/data/raster_source/temporal_multi_raster_source.py b/rastervision_core/rastervision/core/data/raster_source/temporal_multi_raster_source.py
index e3ad95052..79672a4ba 100644
--- a/rastervision_core/rastervision/core/data/raster_source/temporal_multi_raster_source.py
+++ b/rastervision_core/rastervision/core/data/raster_source/temporal_multi_raster_source.py
@@ -1,6 +1,6 @@
 from typing import Any, Optional, Sequence, Tuple
-from pydantic import conint
 
+from pydantic import NonNegativeInt as NonNegInt
 import numpy as np
 
 from rastervision.core.box import Box
@@ -14,7 +14,7 @@ class TemporalMultiRasterSource(MultiRasterSource):
 
     def __init__(self,
                  raster_sources: Sequence[RasterSource],
-                 primary_source_idx: conint(ge=0) = 0,
+                 primary_source_idx: NonNegInt = 0,
                  force_same_dtype: bool = False,
                  raster_transformers: Sequence = [],
                  bbox: Optional[Box] = None):
diff --git a/rastervision_core/rastervision/core/data/vector_transformer/class_inference_transformer_config.py b/rastervision_core/rastervision/core/data/vector_transformer/class_inference_transformer_config.py
index c0dd71b38..8dda0e00b 100644
--- a/rastervision_core/rastervision/core/data/vector_transformer/class_inference_transformer_config.py
+++ b/rastervision_core/rastervision/core/data/vector_transformer/class_inference_transformer_config.py
@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Dict, Optional
+from typing import TYPE_CHECKING
 
 from rastervision.pipeline.config import register_config, Field
 from rastervision.core.data.vector_transformer import (
@@ -12,13 +12,13 @@
 class ClassInferenceTransformerConfig(VectorTransformerConfig):
     """Configure a :class:`.ClassInferenceTransformer`."""
 
-    default_class_id: Optional[int] = Field(
+    default_class_id: int | None = Field(
         None,
         description='The default ``class_id`` to use if class cannot be '
         'inferred using other mechanisms. If a feature has an inferred '
         '``class_id`` of ``None``, then it will be deleted. '
         'Defaults to ``None``.')
-    class_id_to_filter: Optional[Dict[int, list]] = Field(
+    class_id_to_filter: dict[int, list] | None = Field(
         None,
         description='Map from ``class_id`` to JSON filter used to infer '
         'missing class IDs. Each key should be a class ID, and its value '
@@ -28,7 +28,7 @@ class ClassInferenceTransformerConfig(VectorTransformerConfig):
         'is that described by '
         'https://docs.mapbox.com/mapbox-gl-js/style-spec/other/#other-filter. '
         'Defaults to ``None``.')
-    class_name_mapping: dict[str, str] = Field(
+    class_name_mapping: dict[str, str] | None = Field(
         None,
         description='``old_name --> new_name`` mapping for values in the '
         '``class_name`` or ``label`` property of the GeoJSON features. The '
@@ -36,7 +36,7 @@ class ClassInferenceTransformerConfig(VectorTransformerConfig):
         'can also be used to merge multiple classes into one e.g.: '
         '``dict(car="vehicle", truck="vehicle")``. Defaults to None.')
 
-    def build(self, class_config: Optional['ClassConfig'] = None
+    def build(self, class_config: 'ClassConfig | None' = None
               ) -> ClassInferenceTransformer:
         return ClassInferenceTransformer(
             self.default_class_id,
diff --git a/rastervision_core/rastervision/core/rv_pipeline/chip_options.py b/rastervision_core/rastervision/core/rv_pipeline/chip_options.py
index d15440581..ebcc32b9d 100644
--- a/rastervision_core/rastervision/core/rv_pipeline/chip_options.py
+++ b/rastervision_core/rastervision/core/rv_pipeline/chip_options.py
@@ -1,15 +1,13 @@
-from typing import (Any, Dict, Literal, Optional, Tuple, Union)
+from typing import (Any, Dict, Literal, Optional, Self, Tuple, Union)
 from enum import Enum
 
-from pydantic import (PositiveInt as PosInt, conint)
+from pydantic import NonNegativeInt as NonNegInt, PositiveInt as PosInt
 import numpy as np
 
-from rastervision.core.utils.misc import Proportion
 from rastervision.core.rv_pipeline.utils import nodata_below_threshold
+from rastervision.core.utils import Proportion
 from rastervision.pipeline.config import (Config, ConfigError, Field,
-                                          register_config, root_validator)
-
-NonNegInt = conint(ge=0)
+                                          register_config, model_validator)
 
 
 class WindowSamplingMethod(Enum):
@@ -88,33 +86,25 @@ class WindowSamplingConfig(Config):
         'that lie fully within the AOI. If False, windows only partially '
         'intersecting the AOI will also be allowed.')
 
-    @root_validator(skip_on_failure=True)
-    def validate_options(cls, values: dict) -> dict:
-        method = values.get('method')
-        size = values.get('size')
+    @model_validator(mode='after')
+    def validate_options(self) -> Self:
+        method = self.method
+        size = self.size
         if method == WindowSamplingMethod.sliding:
-            has_stride = values.get('stride') is not None
-
-            if not has_stride:
-                values['stride'] = size
+            if self.stride is None:
+                self.stride = size
         elif method == WindowSamplingMethod.random:
-            size_lims = values.get('size_lims')
-            h_lims = values.get('h_lims')
-            w_lims = values.get('w_lims')
-
-            has_size_lims = size_lims is not None
-            has_h_lims = h_lims is not None
-            has_w_lims = w_lims is not None
-
+            has_size_lims = self.size_lims is not None
+            has_h_lims = self.h_lims is not None
+            has_w_lims = self.w_lims is not None
             if not (has_size_lims or has_h_lims or has_w_lims):
-                size_lims = (size, size + 1)
+                self.size_lims = (size, size + 1)
                 has_size_lims = True
-                values['size_lims'] = size_lims
             if has_size_lims == (has_w_lims or has_h_lims):
                 raise ConfigError('Specify either size_lims or h and w lims.')
             if has_h_lims != has_w_lims:
                 raise ConfigError('h_lims and w_lims must both be specified')
-        return values
+        return self
 
 
 @register_config('chip_options')
diff --git a/rastervision_core/rastervision/core/rv_pipeline/object_detection_config.py b/rastervision_core/rastervision/core/rv_pipeline/object_detection_config.py
index 4e7cc51ac..16cbc4741 100644
--- a/rastervision_core/rastervision/core/rv_pipeline/object_detection_config.py
+++ b/rastervision_core/rastervision/core/rv_pipeline/object_detection_config.py
@@ -1,6 +1,7 @@
-from typing import Optional
+from typing import Optional, Self
 
-from rastervision.pipeline.config import Field, register_config, validator
+from rastervision.pipeline.config import (Field, register_config,
+                                          model_validator)
 from rastervision.core.rv_pipeline import (
     ChipOptions, RVPipelineConfig, PredictOptions, WindowSamplingConfig)
 from rastervision.core.data.label_store import ObjectDetectionGeoJSONStoreConfig
@@ -59,20 +60,19 @@ class ObjectDetectionPredictOptions(PredictOptions):
         ('Predicted boxes are only output if their score is above score_thresh.'
          ))
 
-    @validator('stride', always=True)
-    def validate_stride(cls, v: Optional[int], values: dict) -> dict:
-        if v is None:
-            chip_sz: int = values['chip_sz']
-            return chip_sz // 2
-        return v
+    @model_validator(mode='after')
+    def validate_stride(self) -> Self:
+        if self.stride is None:
+            self.stride = self.chip_sz // 2
+        return self
 
 
 @register_config('object_detection')
 class ObjectDetectionConfig(RVPipelineConfig):
     """Configure an :class:`.ObjectDetection` pipeline."""
 
-    chip_options: Optional[ObjectDetectionChipOptions]
-    predict_options: Optional[ObjectDetectionPredictOptions]
+    chip_options: Optional[ObjectDetectionChipOptions] = None
+    predict_options: Optional[ObjectDetectionPredictOptions] = None
 
     def build(self, tmp_dir):
         from rastervision.core.rv_pipeline.object_detection import ObjectDetection
diff --git a/rastervision_core/rastervision/core/rv_pipeline/rv_pipeline_config.py b/rastervision_core/rastervision/core/rv_pipeline/rv_pipeline_config.py
index 3a1b3fb34..468a7942e 100644
--- a/rastervision_core/rastervision/core/rv_pipeline/rv_pipeline_config.py
+++ b/rastervision_core/rastervision/core/rv_pipeline/rv_pipeline_config.py
@@ -1,4 +1,4 @@
-from typing import (TYPE_CHECKING, List, Optional)
+from typing import (TYPE_CHECKING, List, Optional, Self)
 from os.path import join
 
 from rastervision.pipeline.pipeline_config import PipelineConfig
@@ -10,7 +10,7 @@
 from rastervision.core.analyzer import AnalyzerConfig
 from rastervision.core.rv_pipeline.chip_options import ChipOptions
 from rastervision.pipeline.config import (Config, Field, register_config,
-                                          validator)
+                                          model_validator)
 
 if TYPE_CHECKING:
     from rastervision.core.backend.backend import Backend  # noqa
@@ -27,12 +27,11 @@ class PredictOptions(Config):
     batch_sz: int = Field(
         8, description='Batch size to use during prediction.')
 
-    @validator('stride', always=True)
-    def validate_stride(cls, v: Optional[int], values: dict) -> dict:
-        if v is None:
-            chip_sz: int = values['chip_sz']
-            return chip_sz
-        return v
+    @model_validator(mode='after')
+    def validate_stride(self) -> Self:
+        if self.stride is None:
+            self.stride = self.chip_sz
+        return self
 
 
 def rv_pipeline_config_upgrader(cfg_dict: dict, version: int) -> dict:
diff --git a/rastervision_core/rastervision/core/rv_pipeline/semantic_segmentation_config.py b/rastervision_core/rastervision/core/rv_pipeline/semantic_segmentation_config.py
index 17fda9fe3..a5e8c490b 100644
--- a/rastervision_core/rastervision/core/rv_pipeline/semantic_segmentation_config.py
+++ b/rastervision_core/rastervision/core/rv_pipeline/semantic_segmentation_config.py
@@ -1,10 +1,11 @@
-from typing import (List, Literal, Optional, Union)
-from pydantic import conint
+from typing import (List, Literal, Optional, Self, Union)
 import logging
 
+from pydantic import NonNegativeInt as NonNegInt
 import numpy as np
 
-from rastervision.pipeline.config import (register_config, Field, validator)
+from rastervision.pipeline.config import (register_config, Field,
+                                          model_validator)
 from rastervision.core.rv_pipeline.rv_pipeline_config import (PredictOptions,
                                                               RVPipelineConfig)
 from rastervision.core.rv_pipeline.chip_options import (ChipOptions,
@@ -81,7 +82,7 @@ class SemanticSegmentationPredictOptions(PredictOptions):
         description='Stride of the sliding window for generating chips. '
         'Allows aggregating multiple predictions for each pixel if less than '
         'the chip size. Defaults to ``chip_sz``.')
-    crop_sz: Optional[Union[conint(gt=0), Literal['auto']]] = Field(
+    crop_sz: Optional[Union[NonNegInt, Literal['auto']]] = Field(
         None,
         description=
         'Number of rows/columns of pixels from the edge of prediction '
@@ -90,22 +91,19 @@ class SemanticSegmentationPredictOptions(PredictOptions):
         'near the edges of chips. If "auto", will be set to half the stride '
         'if stride is less than chip_sz. Defaults to None.')
 
-    @validator('crop_sz')
-    def validate_crop_sz(cls,
-                         v: Optional[Union[conint(gt=0), Literal['auto']]],
-                         values: dict) -> dict:
-        crop_sz = v
-        if crop_sz == 'auto':
-            chip_sz: int = values['chip_sz']
-            stride: int = values['stride']
-            overlap_sz = chip_sz - stride
+    @model_validator(mode='after')
+    def set_auto_crop_sz(self) -> Self:
+        if self.crop_sz == 'auto':
+            if self.stride is None:
+                self.validate_stride()
+            overlap_sz = self.chip_sz - self.stride
             if overlap_sz % 2 == 1:
                 log.warning(
                     'Using crop_sz="auto" but overlap size (chip_sz minus '
                     'stride) is odd. This means that one pixel row/col will '
                     'still overlap after cropping.')
-            crop_sz = overlap_sz // 2
-        return crop_sz
+            self.crop_sz = overlap_sz // 2
+        return self
 
 
 def ss_config_upgrader(cfg_dict: dict, version: int) -> dict:
@@ -124,8 +122,8 @@ def ss_config_upgrader(cfg_dict: dict, version: int) -> dict:
 class SemanticSegmentationConfig(RVPipelineConfig):
     """Configure a :class:`.SemanticSegmentation` pipeline."""
 
-    chip_options: Optional[SemanticSegmentationChipOptions]
-    predict_options: Optional[SemanticSegmentationPredictOptions]
+    chip_options: SemanticSegmentationChipOptions | None = None
+    predict_options: SemanticSegmentationPredictOptions | None = None
 
     def build(self, tmp_dir):
         from rastervision.core.rv_pipeline.semantic_segmentation import (
diff --git a/rastervision_core/rastervision/core/utils/__init__.py b/rastervision_core/rastervision/core/utils/__init__.py
index fdc6d5d60..114b3024c 100644
--- a/rastervision_core/rastervision/core/utils/__init__.py
+++ b/rastervision_core/rastervision/core/utils/__init__.py
@@ -1,4 +1,4 @@
 # flake8: noqa
 
 from rastervision.core.utils.stac import *
-from rastervision.core.utils.misc import *
+from rastervision.core.utils.types import *
diff --git a/rastervision_core/rastervision/core/utils/misc.py b/rastervision_core/rastervision/core/utils/misc.py
deleted file mode 100644
index dec94f462..000000000
--- a/rastervision_core/rastervision/core/utils/misc.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from pydantic import confloat
-
-Proportion = confloat(ge=0, le=1)
diff --git a/rastervision_core/rastervision/core/utils/types.py b/rastervision_core/rastervision/core/utils/types.py
new file mode 100644
index 000000000..058347860
--- /dev/null
+++ b/rastervision_core/rastervision/core/utils/types.py
@@ -0,0 +1,8 @@
+from typing_extensions import Annotated
+from pydantic.types import StringConstraints
+
+from rastervision.pipeline.config import Field
+
+NonEmptyStr = Annotated[str,
+                        StringConstraints(strip_whitespace=True, min_length=1)]
+Proportion = Annotated[float, Field(ge=0, le=1)]
diff --git a/rastervision_pytorch_learner/rastervision/pytorch_learner/learner_config.py b/rastervision_pytorch_learner/rastervision/pytorch_learner/learner_config.py
index e0aec3912..50d32bfb0 100644
--- a/rastervision_pytorch_learner/rastervision/pytorch_learner/learner_config.py
+++ b/rastervision_pytorch_learner/rastervision/pytorch_learner/learner_config.py
@@ -1,5 +1,5 @@
 from typing import (TYPE_CHECKING, Any, Callable, Dict, Iterable, List,
-                    Literal, Optional, Sequence, Tuple, Union)
+                    Literal, Optional, Self, Sequence, Tuple, Union)
 import os
 from os.path import join, isdir
 from enum import Enum
@@ -7,10 +7,10 @@
 import uuid
 import logging
 
-from pydantic import (PositiveFloat, PositiveInt as PosInt, constr, confloat,
-                      conint)
-from pydantic.utils import sequence_like
-
+from typing_extensions import Annotated
+from pydantic import (NonNegativeInt as NonNegInt, PositiveFloat, PositiveInt
+                      as PosInt, StringConstraints)
+from pydantic.v1.utils import sequence_like
 import albumentations as A
 import torch
 from torch import (nn, optim)
@@ -18,13 +18,15 @@
 from torch.utils.data import Dataset, ConcatDataset, Subset
 
 from rastervision.pipeline.config import (Config, register_config, ConfigError,
-                                          Field, validator, root_validator)
+                                          Field, field_validator,
+                                          model_validator)
 from rastervision.pipeline.file_system import (list_paths, download_if_needed,
                                                unzip, file_exists,
                                                get_local_path, sync_from_dir)
 from rastervision.core.data import (ClassConfig, Scene, DatasetConfig as
                                     SceneDatasetConfig)
 from rastervision.core.rv_pipeline import (WindowSamplingConfig)
+from rastervision.core.utils import NonEmptyStr, Proportion
 from rastervision.pytorch_learner.utils import (
     validate_albumentation_transform, MinMaxNormalize,
     deserialize_albumentation_transform, get_hubconf_dir_from_cfg,
@@ -43,14 +45,11 @@
 ]
 
 # types
-Proportion = confloat(ge=0, le=1)
-NonEmptyStr = constr(strip_whitespace=True, min_length=1)
-NonNegInt = conint(ge=0)
 RGBTuple = Tuple[int, int, int]
 ChannelInds = Sequence[NonNegInt]
 
 
-class Backbone(Enum):
+class Backbone(str, Enum):
     alexnet = 'alexnet'
     densenet121 = 'densenet121'
     densenet169 = 'densenet169'
@@ -136,9 +135,10 @@ class ExternalModuleConfig(Config):
         None,
         description=('Local uri of a zip file, or local uri of a directory,'
                      'or remote uri of zip file.'))
-    github_repo: Optional[constr(
-        strip_whitespace=True, regex=r'.+/.+')] = Field(
-            None, description='<repo-owner>/<repo-name>[:tag]')
+    github_repo: Optional[Annotated[
+        str, StringConstraints(
+            strip_whitespace=True, pattern=r'.+/.+')]] = Field(
+                None, description='<repo-owner>/<repo-name>[:tag]')
     name: Optional[NonEmptyStr] = Field(
         None,
         description=
@@ -157,14 +157,14 @@ class ExternalModuleConfig(Config):
     force_reload: bool = Field(
         False, description='Force reload of module definition.')
 
-    @root_validator(skip_on_failure=True)
-    def check_either_uri_or_repo(cls, values: dict) -> dict:
-        has_uri = values.get('uri') is not None
-        has_repo = values.get('github_repo') is not None
+    @model_validator(mode='after')
+    def check_either_uri_or_repo(self) -> Self:
+        has_uri = self.uri is not None
+        has_repo = self.github_repo is not None
         if has_uri == has_repo:
             raise ConfigError(
                 'Must specify one (and only one) of github_repo and uri.')
-        return values
+        return self
 
     def build(self,
               save_dir: str,
@@ -366,11 +366,11 @@ class SolverConfig(Config):
         description='If specified, the loss will be built from the definition '
         'from this external source, using Torch Hub.')
 
-    @root_validator(skip_on_failure=True)
-    def check_no_loss_opts_if_external(cls, values: dict) -> dict:
-        has_external_loss_def = values.get('external_loss_def') is not None
-        has_ignore_class_index = values.get('ignore_class_index') is not None
-        has_class_loss_weights = values.get('class_loss_weights') is not None
+    @model_validator(mode='after')
+    def check_no_loss_opts_if_external(self) -> Self:
+        has_external_loss_def = self.external_loss_def is not None
+        has_ignore_class_index = self.ignore_class_index is not None
+        has_class_loss_weights = self.class_loss_weights is not None
 
         if has_external_loss_def:
             if has_ignore_class_index:
@@ -379,7 +379,7 @@ def check_no_loss_opts_if_external(cls, values: dict) -> dict:
             if has_class_loss_weights:
                 raise ConfigError('class_loss_weights is not supported '
                                   'with external_loss_def.')
-        return values
+        return self
 
     def build_loss(self,
                    num_classes: int,
@@ -576,8 +576,7 @@ class PlotOptions(Config):
              'for that group.'))
 
     # validators
-    _tf = validator(
-        'transform', allow_reuse=True)(validate_albumentation_transform)
+    _tf = field_validator('transform')(validate_albumentation_transform)
 
     def update(self, **kwargs) -> None:
         super().update()
@@ -586,7 +585,8 @@ def update(self, **kwargs) -> None:
             self.channel_display_groups = get_default_channel_display_groups(
                 img_channels)
 
-    @validator('channel_display_groups')
+    @field_validator('channel_display_groups')
+    @classmethod
     def validate_channel_display_groups(
             cls, v: Optional[Union[Dict[str, Sequence[NonNegInt]], Sequence[
                 Sequence[NonNegInt]]]]
@@ -671,26 +671,24 @@ def num_classes(self):
         return len(self.class_config)
 
     # validators
-    _base_tf = validator(
-        'base_transform', allow_reuse=True)(validate_albumentation_transform)
-    _aug_tf = validator(
-        'aug_transform', allow_reuse=True)(validate_albumentation_transform)
-
-    @validator('augmentors', each_item=True)
-    def validate_augmentors(cls, v: str) -> str:
-        if v not in augmentors:
-            raise ConfigError(f'Unsupported augmentor "{v}"')
+    _base_tf = field_validator('base_transform')(
+        validate_albumentation_transform)
+    _aug_tf = field_validator('aug_transform')(
+        validate_albumentation_transform)
+
+    @field_validator('augmentors')
+    @classmethod
+    def validate_augmentors(cls, v: list[str]) -> str:
+        for aug_name in v:
+            if aug_name not in augmentors:
+                raise ConfigError(f'Unsupported augmentor "{aug_name}"')
         return v
 
-    @root_validator(skip_on_failure=True)
-    def validate_plot_options(cls, values: dict) -> dict:
-        plot_options: Optional[PlotOptions] = values.get('plot_options')
-        if plot_options is None:
-            return None
-        img_channels: Optional[PosInt] = values.get('img_channels')
-        if img_channels is not None:
-            plot_options.update(img_channels=img_channels)
-        return values
+    @model_validator(mode='after')
+    def validate_plot_options(self) -> Self:
+        if self.plot_options is not None and self.img_channels is not None:
+            self.plot_options.update(img_channels=self.img_channels)
+        return self
 
     def get_custom_albumentations_transforms(self) -> List[dict]:
         """Returns all custom transforms found in this config.
@@ -833,11 +831,11 @@ class ImageDataConfig(DataConfig):
         'that will be used for all groups or a list of values '
         '(one for each group).')
 
-    @root_validator(skip_on_failure=True)
-    def validate_group_uris(cls, values: dict) -> dict:
-        group_train_sz = values.get('group_train_sz')
-        group_train_sz_rel = values.get('group_train_sz_rel')
-        group_uris = values.get('group_uris')
+    @model_validator(mode='after')
+    def validate_group_uris(self) -> Self:
+        group_train_sz = self.group_train_sz
+        group_train_sz_rel = self.group_train_sz_rel
+        group_uris = self.group_uris
 
         has_group_train_sz = group_train_sz is not None
         has_group_train_sz_rel = group_train_sz_rel is not None
@@ -858,7 +856,7 @@ def validate_group_uris(cls, values: dict) -> dict:
             if len(group_train_sz_rel) != len(group_uris):
                 raise ConfigError(
                     'len(group_train_sz_rel) != len(group_uris).')
-        return values
+        return self
 
     def _build_dataset(self,
                        dirs: Iterable[str],
@@ -1188,35 +1186,31 @@ def __repr_args__(self):
         out = [('scene_dataset', ds_str), ('sampling', sampling_str)]
         return out
 
-    @validator('sampling')
-    def validate_sampling(
-            cls,
-            v: Union[WindowSamplingConfig, Dict[str, WindowSamplingConfig]],
-            values: dict
-    ) -> Union[WindowSamplingConfig, Dict[str, WindowSamplingConfig]]:
-        if isinstance(v, dict):
-            if len(v) == 0:
-                return v
-            scene_dataset: Optional['SceneDatasetConfig'] = values.get(
-                'scene_dataset')
-            if scene_dataset is None:
-                raise ConfigError('sampling is a non-empty dict but '
-                                  'scene_dataset is None.')
-            for s in scene_dataset.all_scenes:
-                if s.id not in v:
-                    raise ConfigError(
-                        f'Window config not found for scene {s.id}')
-        return v
+    @model_validator(mode='after')
+    def validate_sampling(self) -> Self:
+        if not isinstance(self.sampling, dict):
+            return self
+
+        # empty dict
+        if len(self.sampling) == 0:
+            return self
+
+        if self.scene_dataset is None:
+            raise ConfigError('sampling is a non-empty dict but '
+                              'scene_dataset is None.')
+
+        for s in self.scene_dataset.all_scenes:
+            if s.id not in self.sampling:
+                raise ConfigError(
+                    f'Window sampling config not found for scene: {s.id}')
 
-    @root_validator(skip_on_failure=True)
-    def get_class_config_from_dataset_if_needed(cls, values: dict) -> dict:
-        has_class_config = values.get('class_config') is not None
-        if has_class_config:
-            return values
-        has_scene_dataset = values.get('scene_dataset') is not None
-        if has_scene_dataset:
-            values['class_config'] = values['scene_dataset'].class_config
-        return values
+        return self
+
+    @model_validator(mode='after')
+    def get_class_config_from_dataset_if_needed(self) -> Self:
+        if self.class_config is None and self.scene_dataset is not None:
+            self.class_config = self.scene_dataset.class_config
+        return self
 
     def build_scenes(self,
                      scene_configs: Iterable['SceneConfig'],
@@ -1386,28 +1380,26 @@ class LearnerConfig(Config):
             'last epoch are stored as `model-ckpt-epoch-{N}.pth` where `N` '
             'is the epoch number.'))
 
-    @validator('run_tensorboard')
-    def validate_run_tensorboard(cls, v: bool, values: dict) -> bool:
-        if v and not values.get('log_tensorboard'):
+    @model_validator(mode='after')
+    def validate_run_tensorboard(self) -> Self:
+        if self.run_tensorboard and not self.log_tensorboard:
             raise ConfigError(
                 'Cannot run tensorboard if log_tensorboard is False')
-        return v
+        return self
 
-    @root_validator(skip_on_failure=True)
-    def validate_class_loss_weights(cls, values: dict) -> dict:
-        solver: Optional[SolverConfig] = values.get('solver')
-        if solver is None:
-            return values
-        class_loss_weights = solver.class_loss_weights
+    @model_validator(mode='after')
+    def validate_class_loss_weights(self) -> Self:
+        if self.solver is None:
+            return self
+        class_loss_weights = self.solver.class_loss_weights
         if class_loss_weights is not None:
-            data: DataConfig = values.get('data')
             num_weights = len(class_loss_weights)
-            num_classes = data.num_classes
+            num_classes = self.data.num_classes
             if num_weights != num_classes:
                 raise ConfigError(
                     f'class_loss_weights ({num_weights}) must be same length as '
                     f'the number of classes ({num_classes})')
-        return values
+        return self
 
     def build(self,
               tmp_dir: Optional[str] = None,
diff --git a/rastervision_pytorch_learner/rastervision/pytorch_learner/object_detection_learner_config.py b/rastervision_pytorch_learner/rastervision/pytorch_learner/object_detection_learner_config.py
index af4c1a5dd..271c1645f 100644
--- a/rastervision_pytorch_learner/rastervision/pytorch_learner/object_detection_learner_config.py
+++ b/rastervision_pytorch_learner/rastervision/pytorch_learner/object_detection_learner_config.py
@@ -11,7 +11,7 @@
 from rastervision.core.data import Scene
 from rastervision.core.rv_pipeline import WindowSamplingMethod
 from rastervision.pipeline.config import (Config, register_config, Field,
-                                          validator, ConfigError)
+                                          field_validator, ConfigError)
 from rastervision.pytorch_learner.learner_config import (
     LearnerConfig, ModelConfig, Backbone, ImageDataConfig, GeoDataConfig)
 from rastervision.pytorch_learner.dataset import (
@@ -131,7 +131,8 @@ class ObjectDetectionModelConfig(ModelConfig):
         ('The torchvision.models backbone to use, which must be in the resnet* '
          'family.'))
 
-    @validator('backbone')
+    @field_validator('backbone')
+    @classmethod
     def only_valid_backbones(cls, v):
         if v not in [
                 Backbone.resnet18, Backbone.resnet34, Backbone.resnet50,
@@ -221,7 +222,8 @@ def build(self,
             loss_def_path=loss_def_path,
             training=training)
 
-    @validator('solver')
+    @field_validator('solver')
+    @classmethod
     def validate_solver_config(cls, v: 'SolverConfig') -> 'SolverConfig':
         if v.ignore_class_index is not None:
             raise ConfigError(
diff --git a/rastervision_pytorch_learner/rastervision/pytorch_learner/regression_learner_config.py b/rastervision_pytorch_learner/rastervision/pytorch_learner/regression_learner_config.py
index aa59b6221..7f8439687 100644
--- a/rastervision_pytorch_learner/rastervision/pytorch_learner/regression_learner_config.py
+++ b/rastervision_pytorch_learner/rastervision/pytorch_learner/regression_learner_config.py
@@ -150,7 +150,7 @@ def forward(self, x: 'torch.Tensor') -> 'torch.Tensor':
 class RegressionModelConfig(ModelConfig):
     """Configure a regression model."""
 
-    output_multiplier: List[float] = None
+    output_multiplier: list[float] | None = None
 
     def update(self, learner=None):
         if learner is not None and self.output_multiplier is None:
diff --git a/rastervision_pytorch_learner/rastervision/pytorch_learner/semantic_segmentation_learner_config.py b/rastervision_pytorch_learner/rastervision/pytorch_learner/semantic_segmentation_learner_config.py
index f7131432e..a8371a0c8 100644
--- a/rastervision_pytorch_learner/rastervision/pytorch_learner/semantic_segmentation_learner_config.py
+++ b/rastervision_pytorch_learner/rastervision/pytorch_learner/semantic_segmentation_learner_config.py
@@ -11,7 +11,7 @@
 from rastervision.core.data import Scene
 from rastervision.core.rv_pipeline import WindowSamplingMethod
 from rastervision.pipeline.config import (Config, register_config, Field,
-                                          validator, ConfigError)
+                                          field_validator, ConfigError)
 from rastervision.pytorch_learner.learner_config import (
     Backbone, LearnerConfig, ModelConfig, ImageDataConfig, GeoDataConfig)
 from rastervision.pytorch_learner.dataset import (
@@ -165,7 +165,8 @@ class SemanticSegmentationModelConfig(ModelConfig):
         description='The torchvision.models backbone to use. Currently, only '
         'resnet50 and resnet101 are supported.')
 
-    @validator('backbone')
+    @field_validator('backbone')
+    @classmethod
     def only_valid_backbones(cls, v):
         if v not in [Backbone.resnet50, Backbone.resnet101]:
             raise ValueError(