diff --git a/docs/apis/models/semantic_segmentation.md b/docs/apis/models/semantic_segmentation.md
index ab62bd57fa25f4ed2bc35551996688de0cedcacc..b46a6273c660a017700c00598515891904fb9dde 100755
--- a/docs/apis/models/semantic_segmentation.md
+++ b/docs/apis/models/semantic_segmentation.md
@@ -110,6 +110,34 @@ batch_predict(self, img_file_list, transforms=None):
 > > - **dict**: 每个元素都为列表，表示各图像的预测结果。各图像的预测结果用字典表示，包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes)。
 
 
+### overlap_tile_predict
+
+```
+overlap_tile_predict(self, img_file, tile_size=[512, 512], pad_size=[64, 64], batch_size=32, transforms=None)
+```
+
+> DeepLabv3p模型的滑动预测接口, 支持有重叠和无重叠两种方式。
+
+> **无重叠的滑动窗口预测**：在输入图片上以固定大小的窗口滑动，分别对每个窗口下的图像进行预测，最后将各窗口的预测结果拼接成输入图片的预测结果。**使用时需要把参数`pad_size`设置为`[0, 0]`**。
+
+> **有重叠的滑动窗口预测**：在Unet论文中，作者提出一种有重叠的滑动窗口预测策略（Overlap-tile strategy）来消除拼接处的裂痕感。对各滑动窗口预测时，会向四周扩展一定的面积，对扩展后的窗口进行预测，例如下图中的蓝色部分区域，到拼接时只取各窗口中间部分的预测结果，例如下图中的黄色部分区域。位于输入图像边缘处的窗口，其扩展面积下的像素则通过将边缘部分像素镜像填补得到。
+
+![](../../../examples/remote_sensing/images/overlap_tile.png)
+
+> 需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`DeepLabv3p.test_transforms`和`DeepLabv3p.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`overlap_tile_predict`接口时，用户需要再重新定义test_transforms传入给`overlap_tile_predict`接口。
+
+> **参数**
+> >
+> > - **img_file** (str|np.ndarray): 预测图像路径或numpy数组(HWC排列，BGR格式)。
+> > - **tile_size** (list|tuple): 滑动窗口的大小，该区域内用于拼接预测结果，格式为（W，H）。默认值为[512, 512]。
+> > - **pad_size** (list|tuple): 滑动窗口向四周扩展的大小，扩展区域内不用于拼接预测结果，格式为（W，H）。默认值为[64, 64]。
+> > - **batch_size** (int)：对窗口进行批量预测时的批量大小。默认值为32。
+> > - **transforms** (paddlex.seg.transforms): 数据预处理操作。
+
+> **返回值**
+> >
+> > - **dict**: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes)。
+
 
 ## paddlex.seg.UNet
 
@@ -133,6 +161,7 @@ paddlex.seg.UNet(num_classes=2, upsample_mode='bilinear', use_bce_loss=False, us
 > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate)
 > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict)
 > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict)
+> - overlap_tile_predict 滑动窗口预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict)
 
 ## paddlex.seg.HRNet
 
@@ -156,6 +185,7 @@ paddlex.seg.HRNet(num_classes=2, width=18, use_bce_loss=False, use_dice_loss=Fal
 > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate)
 > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict)
 > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict)
+> - overlap_tile_predict 滑动窗预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict)
 
 ## paddlex.seg.FastSCNN
 
@@ -179,3 +209,4 @@ paddlex.seg.FastSCNN(num_classes=2, use_bce_loss=False, use_dice_loss=False, cla
 > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate)
 > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict)
 > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict)
+> - overlap_tile_predict 滑动窗预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict)
diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index 8d7e68ee64a2f8cb47e9c489d018664054a568d6..f232951fdc7ee1301043140ad792bf977fee5853 100755
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -13,3 +13,4 @@ PaddleX精选飞桨视觉开发套件在产业实践中的成熟模型结构，
    meter_reader.md
    human_segmentation.md
    multi-channel_remote_sensing/README.md
+   remote_sensing.md
diff --git a/docs/examples/multi-channel_remote_sensing/README.md b/docs/examples/multi-channel_remote_sensing/README.md
index 1a46a6133e1cf75803c0d8a646840d096c21ee24..cfa91aa608483bea9d0b786a89f34d5feec029c2 100644
--- a/docs/examples/multi-channel_remote_sensing/README.md
+++ b/docs/examples/multi-channel_remote_sensing/README.md
@@ -7,7 +7,7 @@
 ## 前置依赖
 * Paddle paddle >= 1.8.4
 * Python >= 3.5
-* PaddleX >= 1.1.0
+* PaddleX >= 1.1.4
 
 安装的相关问题参考[PaddleX安装](../../install.md)
 
diff --git a/docs/examples/remote_sensing.md b/docs/examples/remote_sensing.md
new file mode 100644
index 0000000000000000000000000000000000000000..26c13b005fdf8642f232132266f943855e9f50e9
--- /dev/null
+++ b/docs/examples/remote_sensing.md
@@ -0,0 +1,82 @@
+# RGB遥感影像分割
+
+本案例基于PaddleX实现遥感影像分割，提供滑动窗口预测方式，以避免在直接对大尺寸图片进行预测时显存不足的发生。此外，滑动窗口之间的重叠程度可配置，以此消除最终预测结果中各窗口拼接处的裂痕感。
+
+## 前置依赖
+
+* Paddle paddle >= 1.8.4
+* Python >= 3.5
+* PaddleX >= 1.1.4
+
+安装的相关问题参考[PaddleX安装](../install.md)
+
+下载PaddleX源码:
+
+```
+git clone https://github.com/PaddlePaddle/PaddleX
+```
+
+该案例所有脚本均位于`PaddleX/examples/remote_sensing/`，进入该目录：
+
+```
+cd PaddleX/examples/remote_sensing/
+```
+
+## 数据准备
+
+本案例使用2015 CCF大数据比赛提供的高清遥感影像，包含5张带标注的RGB图像，图像尺寸最大有7969 × 7939、最小有4011 × 2470。该数据集共标注了5类物体，分别是背景（标记为0）、植被（标记为1）、建筑（标记为2）、水体（标记为3）、道路 （标记为4）。
+
+本案例将前4张图片划分入训练集，第5张图片作为验证集。为增加训练时的批量大小，以滑动窗口为(1024，1024)、步长为(512, 512)对前4张图片进行切分，加上原本的4张大尺寸图片，训练集一共有688张图片。在训练过程中直接对大图片进行验证会导致显存不足，为避免此类问题的出现，针对验证集以滑动窗口为(769, 769)、步长为(769，769)对第5张图片进行切分，得到40张子图片。
+
+运行以下脚本，下载原始数据集，并完成数据集的切分：
+
+```
+python3 prepare_data.py
+```
+
+## 模型训练
+
+分割模型选择Backbone为MobileNetv3_large_ssld的Deeplabv3模型，该模型兼备高性能高精度的优点。运行以下脚本，进行模型训练：
+```
+python3 train.py
+```
+
+也可以跳过模型训练步骤，直接下载预训练模型进行后续的模型预测和评估：
+```
+wget https://bj.bcebos.com/paddlex/examples/remote_sensing/models/ccf_remote_model.tar.gz
+tar -xvf ccf_remote_model.tar.gz
+```
+
+## 模型预测
+
+直接对大尺寸图片进行预测会导致显存不足，为避免此类问题的出现，本案例提供了滑动窗口预测接口，支持有重叠和无重叠两种方式。
+
+* 无重叠的滑动窗口预测
+
+在输入图片上以固定大小的窗口滑动，分别对每个窗口下的图像进行预测，最后将各窗口的预测结果拼接成输入图片的预测结果。由于每个窗口边缘部分的预测效果会比中间部分的差，因此每个窗口拼接处可能会有明显的裂痕感。
+
+该预测方式的API接口详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict)，**使用时需要把参数`pad_size`设置为`[0, 0]`**。
+
+* 有重叠的滑动窗口预测
+
+在Unet论文中，作者提出一种有重叠的滑动窗口预测策略（Overlap-tile strategy）来消除拼接处的裂痕感。对各滑动窗口预测时，会向四周扩展一定的面积，对扩展后的窗口进行预测，例如下图中的蓝色部分区域，到拼接时只取各窗口中间部分的预测结果，例如下图中的黄色部分区域。位于输入图像边缘处的窗口，其扩展面积下的像素则通过将边缘部分像素镜像填补得到。
+
+该预测方式的API接口说明详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict)。
+
+![](../../examples/remote_sensing/images/overlap_tile.png)
+
+相比无重叠的滑动窗口预测，有重叠的滑动窗口预测策略将本案例的模型精度miou从80.58%提升至81.52%，并且将预测可视化结果中裂痕感显著消除，可见下图中两种预测方式的效果对比。
+
+![](../../examples/remote_sensing/images/visualize_compare.jpg)
+
+运行以下脚本使用有重叠的滑动窗口进行预测：
+```
+python3 predict.py
+```
+
+## 模型评估
+
+在训练过程中，每隔10个迭代轮数会评估一次模型在验证集的精度。由于已事先将原始大尺寸图片切分成小块，此时相当于使用无重叠的大图切小图预测方式，最优模型精度miou为80.58%。运行以下脚本，将采用有重叠的大图切小图的预测方式，重新评估原始大尺寸图片的模型精度，此时miou为81.52%。
+```
+python3 eval.py
+```
diff --git a/docs/examples/remote_sensing/index.rst b/docs/examples/remote_sensing/index.rst
deleted file mode 100755
index dc375659be121c4bd04843fd281416a4d00ad865..0000000000000000000000000000000000000000
--- a/docs/examples/remote_sensing/index.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-遥感分割案例
-=======================================
-
-
-这里面写遥感分割案例，可根据需求拆分为多个文档
diff --git a/examples/multi-channel_remote_sensing/README.md b/examples/multi-channel_remote_sensing/README.md
index 8554e3d858ad7101d125d066ad1df19095eb2525..63ec786dee429652f16c472389708919ee33f4a7 100644
--- a/examples/multi-channel_remote_sensing/README.md
+++ b/examples/multi-channel_remote_sensing/README.md
@@ -14,7 +14,7 @@
 
 * Paddle paddle >= 1.8.4
 * Python >= 3.5
-* PaddleX >= 1.1.0
+* PaddleX >= 1.1.4
 
 安装的相关问题参考[PaddleX安装](../../docs/install.md)
 
diff --git a/examples/remote_sensing/README.md b/examples/remote_sensing/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2663754d1995bc810f1fafb838c4045ab4761faa
--- /dev/null
+++ b/examples/remote_sensing/README.md
@@ -0,0 +1,88 @@
+# RGB遥感影像分割
+
+本案例基于PaddleX实现遥感影像分割，提供滑动窗口预测方式，以避免在直接对大尺寸图片进行预测时显存不足的发生。此外，滑动窗口之间的重叠程度可配置，以此消除最终预测结果中各窗口拼接处的裂痕感。
+
+## 目录
+* [数据准备](#1)
+* [模型训练](#2)
+* [模型预测](#3)
+* [模型评估](#4)
+
+#### 前置依赖
+
+* Paddle paddle >= 1.8.4
+* Python >= 3.5
+* PaddleX >= 1.1.4
+
+安装的相关问题参考[PaddleX安装](../install.md)
+
+下载PaddleX源码:
+
+```
+git clone https://github.com/PaddlePaddle/PaddleX
+```
+
+该案例所有脚本均位于`PaddleX/examples/remote_sensing/`，进入该目录：
+
+```
+cd PaddleX/examples/remote_sensing/
+```
+
+## <h2 id="1">数据准备</h2>
+
+本案例使用2015 CCF大数据比赛提供的高清遥感影像，包含5张带标注的RGB图像，图像尺寸最大有7969 × 7939、最小有4011 × 2470。该数据集共标注了5类物体，分别是背景（标记为0）、植被（标记为1）、建筑（标记为2）、水体（标记为3）、道路 （标记为4）。
+
+本案例将前4张图片划分入训练集，第5张图片作为验证集。为增加训练时的批量大小，以滑动窗口为(1024，1024)、步长为(512, 512)对前4张图片进行切分，加上原本的4张大尺寸图片，训练集一共有688张图片。在训练过程中直接对大图片进行验证会导致显存不足，为避免此类问题的出现，针对验证集以滑动窗口为(769, 769)、步长为(769，769)对第5张图片进行切分，得到40张子图片。
+
+运行以下脚本，下载原始数据集，并完成数据集的切分：
+
+```
+python3 prepare_data.py
+```
+
+## <h2 id="2">模型训练</h2>
+
+分割模型选择Backbone为MobileNetv3_large_ssld的Deeplabv3模型，该模型兼备高性能高精度的优点。运行以下脚本，进行模型训练：
+```
+python3 train.py
+```
+
+也可以跳过模型训练步骤，直接下载预训练模型进行后续的模型预测和评估：
+```
+wget https://bj.bcebos.com/paddlex/examples/remote_sensing/models/ccf_remote_model.tar.gz
+tar -xvf ccf_remote_model.tar.gz
+```
+
+## <h2 id="3">模型预测</h2>
+
+直接对大尺寸图片进行预测会导致显存不足，为避免此类问题的出现，本案例提供了滑动窗口预测接口，支持有重叠和无重叠两种方式。
+
+* 无重叠的滑动窗口预测
+
+在输入图片上以固定大小的窗口滑动，分别对每个窗口下的图像进行预测，最后将各窗口的预测结果拼接成输入图片的预测结果。由于每个窗口边缘部分的预测效果会比中间部分的差，因此每个窗口拼接处可能会有明显的裂痕感。
+
+该预测方式的API接口详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict)，**使用时需要把参数`pad_size`设置为`[0, 0]`**。
+
+* 有重叠的滑动窗口预测
+
+在Unet论文中，作者提出一种有重叠的滑动窗口预测策略（Overlap-tile strategy）来消除拼接处的裂痕感。对各滑动窗口预测时，会向四周扩展一定的面积，对扩展后的窗口进行预测，例如下图中的蓝色部分区域，到拼接时只取各窗口中间部分的预测结果，例如下图中的黄色部分区域。位于输入图像边缘处的窗口，其扩展面积下的像素则通过将边缘部分像素镜像填补得到。
+
+该预测方式的API接口说明详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict)。
+
+![](images/overlap_tile.png)
+
+相比无重叠的滑动窗口预测，有重叠的滑动窗口预测策略将本案例的模型精度miou从80.58%提升至81.52%，并且将预测可视化结果中裂痕感显著消除，可见下图中两种预测方式的效果对比。
+
+![](images/visualize_compare.jpg)
+
+运行以下脚本使用有重叠的滑动窗口进行预测：
+```
+python3 predict.py
+```
+
+## <h2 id="4">模型评估</h2>
+
+在训练过程中，每隔10个迭代轮数会评估一次模型在验证集的精度。由于已事先将原始大尺寸图片切分成小块，此时相当于使用无重叠的滑动窗口预测方式，最优模型精度miou为80.58%。运行以下脚本，将采用有重叠的滑动窗口预测方式，重新评估原始大尺寸图片的模型精度，此时miou为81.52%。
+```
+python3 eval.py
+```
diff --git a/examples/remote_sensing/eval.py b/examples/remote_sensing/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..540494501102fdc0ee0e0dd166e8a4ae77863589
--- /dev/null
+++ b/examples/remote_sensing/eval.py
@@ -0,0 +1,44 @@
+# 环境变量配置，用于控制是否使用GPU
+# 说明文档：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import numpy as np
+import cv2
+from PIL import Image
+from collections import OrderedDict
+
+import paddlex as pdx
+import paddlex.utils.logging as logging
+from paddlex.cv.models.utils.seg_eval import ConfusionMatrix
+
+
+def update_confusion_matrix(confusion_matrix, predction, label):
+    pred = predction["label_map"]
+    pred = pred[np.newaxis, :, :, np.newaxis]
+    pred = pred.astype(np.int64)
+    label = label[np.newaxis, np.newaxis, :, :]
+    mask = label != model.ignore_index
+    confusion_matrix.calculate(pred=pred, label=label, ignore=mask)
+
+
+model_dir = 'output/deeplabv3p_mobilenetv3_large_ssld/best_model'
+img_file = "dataset/JPEGImages/5.png"
+label_file = "dataset/Annotations/5_class.png"
+
+model = pdx.load_model(model_dir)
+
+conf_mat = ConfusionMatrix(model.num_classes, streaming=True)
+
+# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict
+overlap_tile_predict = model.overlap_tile_predict(
+    img_file=img_file, tile_size=(769, 769), pad_size=[64, 64], batch_size=32)
+
+label = np.asarray(Image.open(label_file))
+update_confusion_matrix(conf_mat, overlap_tile_predict, label)
+
+category_iou, miou = conf_mat.mean_iou()
+category_acc, macc = conf_mat.accuracy()
+logging.info(
+    "miou={:.6f} category_iou={} macc={:.6f} category_acc={} kappa={:.6f}".
+    format(miou, category_iou, macc, category_acc, conf_mat.kappa()))
diff --git a/examples/remote_sensing/images/overlap_tile.png b/examples/remote_sensing/images/overlap_tile.png
new file mode 100644
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diff --git a/examples/remote_sensing/images/visualize_compare.jpg b/examples/remote_sensing/images/visualize_compare.jpg
new file mode 100644
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diff --git a/examples/remote_sensing/predict.py b/examples/remote_sensing/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..c22eeef3b71518c61efbddab19b809ec8650696e
--- /dev/null
+++ b/examples/remote_sensing/predict.py
@@ -0,0 +1,18 @@
+# 环境变量配置，用于控制是否使用GPU
+# 说明文档：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import paddlex as pdx
+
+model_dir = 'output/deeplabv3p_mobilenetv3_large_ssld/best_model'
+img_file = "dataset/JPEGImages/5.png"
+save_dir = 'output/deeplabv3p_mobilenetv3_large_ssld/'
+
+model = pdx.load_model('output/deeplabv3p_mobilenetv3_large_ssld/best_model')
+
+# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict
+pred = model.overlap_tile_predict(
+    img_file=img_file, tile_size=(769, 769), pad_size=[64, 64], batch_size=32)
+
+pdx.seg.visualize(img_file, pred, weight=0., save_dir=save_dir)
diff --git a/examples/remote_sensing/prepara_data.py b/examples/remote_sensing/prepara_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..6951bf8eb510564e06701c7ddb1c59fb4fc1b25b
--- /dev/null
+++ b/examples/remote_sensing/prepara_data.py
@@ -0,0 +1,95 @@
+import os
+import os.path as osp
+import numpy as np
+import cv2
+import shutil
+from PIL import Image
+import paddlex as pdx
+
+# 定义训练集切分时的滑动窗口大小和步长，格式为(W, H)
+train_tile_size = (1024, 1024)
+train_stride = (512, 512)
+# 定义验证集切分时的滑动窗口大小和步长，格式(W, H)
+val_tile_size = (769, 769)
+val_stride = (769, 769)
+
+# 下载并解压2015 CCF大数据比赛提供的高清遥感影像
+ccf_remote_dataset = 'https://bj.bcebos.com/paddlex/examples/remote_sensing/datasets/ccf_remote_dataset.tar.gz'
+pdx.utils.download_and_decompress(ccf_remote_dataset, path='./')
+
+if not osp.exists('./dataset/JPEGImages'):
+    os.makedirs('./dataset/JPEGImages')
+if not osp.exists('./dataset/Annotations'):
+    os.makedirs('./dataset/Annotations')
+
+# 将前4张图片划分入训练集，并切分成小块之后加入到训练集中
+# 并生成train_list.txt
+for train_id in range(1, 5):
+    shutil.copyfile("ccf_remote_dataset/{}.png".format(train_id),
+                    "./dataset/JPEGImages/{}.png".format(train_id))
+    shutil.copyfile("ccf_remote_dataset/{}_class.png".format(train_id),
+                    "./dataset/Annotations/{}_class.png".format(train_id))
+    mode = 'w' if train_id == 1 else 'a'
+    with open('./dataset/train_list.txt', mode) as f:
+        f.write("JPEGImages/{}.png Annotations/{}_class.png\n".format(
+            train_id, train_id))
+
+for train_id in range(1, 5):
+    image = cv2.imread('ccf_remote_dataset/{}.png'.format(train_id))
+    label = Image.open('ccf_remote_dataset/{}_class.png'.format(train_id))
+    H, W, C = image.shape
+    train_tile_id = 1
+    for h in range(0, H, train_stride[1]):
+        for w in range(0, W, train_stride[0]):
+            left = w
+            upper = h
+            right = min(w + train_tile_size[0] * 2, W)
+            lower = min(h + train_tile_size[1] * 2, H)
+            tile_image = image[upper:lower, left:right, :]
+            cv2.imwrite("./dataset/JPEGImages/{}_{}.png".format(
+                train_id, train_tile_id), tile_image)
+            cut_label = label.crop((left, upper, right, lower))
+            cut_label.save("./dataset/Annotations/{}_class_{}.png".format(
+                train_id, train_tile_id))
+            with open('./dataset/train_list.txt', 'a') as f:
+                f.write("JPEGImages/{}_{}.png Annotations/{}_class_{}.png\n".
+                        format(train_id, train_tile_id, train_id,
+                               train_tile_id))
+            train_tile_id += 1
+
+# 将第5张图片切分成小块之后加入到验证集中
+val_id = 5
+val_tile_id = 1
+shutil.copyfile("ccf_remote_dataset/{}.png".format(val_id),
+                "./dataset/JPEGImages/{}.png".format(val_id))
+shutil.copyfile("ccf_remote_dataset/{}_class.png".format(val_id),
+                "./dataset/Annotations/{}_class.png".format(val_id))
+image = cv2.imread('ccf_remote_dataset/{}.png'.format(val_id))
+label = Image.open('ccf_remote_dataset/{}_class.png'.format(val_id))
+H, W, C = image.shape
+for h in range(0, H, val_stride[1]):
+    for w in range(0, W, val_stride[0]):
+        left = w
+        upper = h
+        right = min(w + val_tile_size[0], W)
+        lower = min(h + val_tile_size[1], H)
+        cut_image = image[upper:lower, left:right, :]
+        cv2.imwrite("./dataset/JPEGImages/{}_{}.png".format(
+            val_id, val_tile_id), cut_image)
+        cut_label = label.crop((left, upper, right, lower))
+        cut_label.save("./dataset/Annotations/{}_class_{}.png".format(
+            val_id, val_tile_id))
+        mode = 'w' if val_tile_id == 1 else 'a'
+        with open('./dataset/val_list.txt', mode) as f:
+            f.write("JPEGImages/{}_{}.png Annotations/{}_class_{}.png\n".
+                    format(val_id, val_tile_id, val_id, val_tile_id))
+        val_tile_id += 1
+
+# 生成labels.txt
+label_list = ['background', 'vegetation', 'building', 'water', 'road']
+for i, label in enumerate(label_list):
+    mode = 'w' if i == 0 else 'a'
+    with open('./dataset/labels.txt', 'a') as f:
+        name = "{}\n".format(label) if i < len(
+            label_list) - 1 else "{}".format(label)
+        f.write(name)
diff --git a/examples/remote_sensing/train.py b/examples/remote_sensing/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a7ff121a9e54648ef8aa754d77360cc14e871f8
--- /dev/null
+++ b/examples/remote_sensing/train.py
@@ -0,0 +1,55 @@
+# 环境变量配置，用于控制是否使用GPU
+# 说明文档：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import paddlex as pdx
+from paddlex.seg import transforms
+
+# 定义训练和验证时的transforms
+# API说明 https://paddlex.readthedocs.io/zh_CN/develop/apis/transforms/seg_transforms.html
+train_transforms = transforms.Compose([
+    transforms.RandomPaddingCrop(crop_size=769),
+    transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(),
+    transforms.Normalize()
+])
+
+eval_transforms = transforms.Compose(
+    [transforms.Padding(target_size=769), transforms.Normalize()])
+
+# 定义训练和验证所用的数据集
+# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/datasets.html#paddlex-datasets-segdataset
+train_dataset = pdx.datasets.SegDataset(
+    data_dir='dataset',
+    file_list='dataset/train_list.txt',
+    label_list='dataset/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+eval_dataset = pdx.datasets.SegDataset(
+    data_dir='dataset',
+    file_list='dataset/val_list.txt',
+    label_list='dataset/labels.txt',
+    transforms=eval_transforms)
+
+## 初始化模型，并进行训练
+## 可使用VisualDL查看训练指标，参考https://paddlex.readthedocs.io/zh_CN/develop/train/visualdl.html
+num_classes = len(train_dataset.labels)
+
+# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#paddlex-seg-deeplabv3p
+model = pdx.seg.DeepLabv3p(
+    num_classes=num_classes,
+    backbone='MobileNetV3_large_x1_0_ssld',
+    pooling_crop_size=(769, 769))
+
+# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#train
+# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+model.train(
+    num_epochs=400,
+    train_dataset=train_dataset,
+    train_batch_size=16,
+    eval_dataset=eval_dataset,
+    learning_rate=0.01,
+    save_interval_epochs=10,
+    pretrain_weights='CITYSCAPES',
+    save_dir='output/deeplabv3p_mobilenetv3_large_ssld',
+    use_vdl=True)
diff --git a/paddlex/cv/datasets/dataset.py b/paddlex/cv/datasets/dataset.py
index 82a29f5443c56c9caab2ad725e72493e0bc4bd51..bedbc5ab63ce8f78fd1a40a5c9c89990e190f21f 100644
--- a/paddlex/cv/datasets/dataset.py
+++ b/paddlex/cv/datasets/dataset.py
@@ -239,9 +239,8 @@ def generate_minibatch(batch_data, label_padding_value=255, mapper=None):
                 _, label_h, label_w = data[1].shape
                 padding_label[:, :label_h, :label_w] = data[1]
                 padding_batch.append((padding_im, padding_label))
-            elif len(data[1]) == 0 or isinstance(
-                    data[1][0],
-                    tuple) and data[1][0][0] in ['resize', 'padding']:
+            elif len(data[1]) == 0 or isinstance(data[1][0], tuple) and data[
+                    1][0][0] in ['origin_shape', 'resize', 'padding']:
                 # padding the image and insert 'padding' into `im_info`
                 # of segmentation during the infering phase
                 if len(data[1]) == 0 or 'padding' not in [
diff --git a/paddlex/cv/models/deeplabv3p.py b/paddlex/cv/models/deeplabv3p.py
index 49a6a1d33e31ccc871df7c02301f40ba606a51dc..8f7341c7107f6103a6c49ca4fc615c41f2231af6 100644
--- a/paddlex/cv/models/deeplabv3p.py
+++ b/paddlex/cv/models/deeplabv3p.py
@@ -24,6 +24,7 @@ import paddlex.utils.logging as logging
 import paddlex
 from paddlex.cv.transforms import arrange_transforms
 from paddlex.cv.datasets import generate_minibatch
+from paddlex.cv.transforms.seg_transforms import Compose
 from collections import OrderedDict
 from .base import BaseAPI
 from .utils.seg_eval import ConfusionMatrix
@@ -448,7 +449,11 @@ class DeepLabv3p(BaseAPI):
         return metrics
 
     @staticmethod
-    def _preprocess(images, transforms, model_type, class_name, thread_pool=None):
+    def _preprocess(images,
+                    transforms,
+                    model_type,
+                    class_name,
+                    thread_pool=None):
         arrange_transforms(
             model_type=model_type,
             class_name=class_name,
@@ -554,3 +559,102 @@ class DeepLabv3p(BaseAPI):
 
         preds = DeepLabv3p._postprocess(result, im_info)
         return preds
+
+    def overlap_tile_predict(self,
+                             img_file,
+                             tile_size=[512, 512],
+                             pad_size=[64, 64],
+                             batch_size=32,
+                             transforms=None):
+        """有重叠的大图切小图预测。
+        Args:
+            img_file(str|np.ndarray): 预测图像路径，或者是解码后的排列格式为（H, W, C）且类型为float32且为BGR格式的数组。
+            tile_size(list|tuple): 滑动窗口的大小，该区域内用于拼接预测结果，格式为（W，H）。默认值为[512, 512]。
+            pad_size(list|tuple): 滑动窗口向四周扩展的大小，扩展区域内不用于拼接预测结果，格式为（W，H）。默认值为[64，64]。
+            batch_size(int)：对窗口进行批量预测时的批量大小。默认值为32
+            transforms(paddlex.cv.transforms): 数据预处理操作。
+
+
+        Returns:
+            dict: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，
+                像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes)
+        """
+
+        if transforms is None and not hasattr(self, 'test_transforms'):
+            raise Exception("transforms need to be defined, now is None.")
+
+        if isinstance(img_file, str):
+            image, _ = Compose.decode_image(img_file, None)
+        elif isinstance(img_file, np.ndarray):
+            image = img_file.copy()
+        else:
+            raise Exception("im_file must be list/tuple")
+
+        height, width, channel = image.shape
+        image_tile_list = list()
+
+        # Padding along the left and right sides
+        if pad_size[0] > 0:
+            left_pad = cv2.flip(image[0:height, 0:pad_size[0], :], 1)
+            right_pad = cv2.flip(image[0:height, -pad_size[0]:width, :], 1)
+            padding_image = cv2.hconcat([left_pad, image])
+            padding_image = cv2.hconcat([padding_image, right_pad])
+        else:
+            import copy
+            padding_image = copy.deepcopy(image)
+
+        # Padding along the upper and lower sides
+        padding_height, padding_width, _ = padding_image.shape
+        if pad_size[1] > 0:
+            upper_pad = cv2.flip(
+                padding_image[0:pad_size[1], 0:padding_width, :], 0)
+            lower_pad = cv2.flip(
+                padding_image[-pad_size[1]:padding_height, 0:padding_width, :],
+                0)
+            padding_image = cv2.vconcat([upper_pad, padding_image])
+            padding_image = cv2.vconcat([padding_image, lower_pad])
+
+        # crop the padding image into tile pieces
+        padding_height, padding_width, _ = padding_image.shape
+
+        for h_id in range(0, height // tile_size[1] + 1):
+            for w_id in range(0, width // tile_size[0] + 1):
+                left = w_id * tile_size[0]
+                upper = h_id * tile_size[1]
+                right = min(left + tile_size[0] + pad_size[0] * 2,
+                            padding_width)
+                lower = min(upper + tile_size[1] + pad_size[1] * 2,
+                            padding_height)
+                image_tile = padding_image[upper:lower, left:right, :]
+                image_tile_list.append(image_tile)
+
+        # predict
+        label_map = np.zeros((height, width), dtype=np.uint8)
+        score_map = np.zeros(
+            (height, width, self.num_classes), dtype=np.float32)
+        num_tiles = len(image_tile_list)
+        for i in range(0, num_tiles, batch_size):
+            begin = i
+            end = min(i + batch_size, num_tiles)
+            res = self.batch_predict(
+                img_file_list=image_tile_list[begin:end],
+                transforms=transforms)
+            for j in range(begin, end):
+                h_id = j // (width // tile_size[0] + 1)
+                w_id = j % (width // tile_size[0] + 1)
+                left = w_id * tile_size[0]
+                upper = h_id * tile_size[1]
+                right = min((w_id + 1) * tile_size[0], width)
+                lower = min((h_id + 1) * tile_size[1], height)
+                tile_label_map = res[j - begin]["label_map"]
+                tile_score_map = res[j - begin]["score_map"]
+                tile_upper = pad_size[1]
+                tile_lower = tile_label_map.shape[0] - pad_size[1]
+                tile_left = pad_size[0]
+                tile_right = tile_label_map.shape[1] - pad_size[0]
+                label_map[upper:lower, left:right] = \
+                    tile_label_map[tile_upper:tile_lower, tile_left:tile_right]
+                score_map[upper:lower, left:right, :] = \
+                    tile_score_map[tile_upper:tile_lower, tile_left:tile_right, :]
+        result = {"label_map": label_map, "score_map": score_map}
+        return result
diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py
index c482930ca18a39a2e684c17d470b931dfc6e5823..a59e405f627e9fa7ab00f12c6eb38668ee0734e5 100644
--- a/paddlex/cv/transforms/seg_transforms.py
+++ b/paddlex/cv/transforms/seg_transforms.py
@@ -723,28 +723,25 @@ class Padding(SegTransform):
             target_width = self.target_size[0]
         pad_height = target_height - im_height
         pad_width = target_width - im_width
-        if pad_height < 0 or pad_width < 0:
-            raise ValueError(
-                'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
-                .format(im_width, im_height, target_width, target_height))
-        else:
-            im = cv2.copyMakeBorder(
-                im,
+        pad_height = max(pad_height, 0)
+        pad_width = max(pad_width, 0)
+        im = cv2.copyMakeBorder(
+            im,
+            0,
+            pad_height,
+            0,
+            pad_width,
+            cv2.BORDER_CONSTANT,
+            value=self.im_padding_value)
+        if label is not None:
+            label = cv2.copyMakeBorder(
+                label,
                 0,
                 pad_height,
                 0,
                 pad_width,
                 cv2.BORDER_CONSTANT,
-                value=self.im_padding_value)
-            if label is not None:
-                label = cv2.copyMakeBorder(
-                    label,
-                    0,
-                    pad_height,
-                    0,
-                    pad_width,
-                    cv2.BORDER_CONSTANT,
-                    value=self.label_padding_value)
+                value=self.label_padding_value)
         if label is None:
             return (im, im_info)
         else:
diff --git a/paddlex/deploy.py b/paddlex/deploy.py
index e7a9264240ff52007ad3480ed794064cc171320f..747cf16454e16d0daa7d5e415a16faee55448ce5 100644
--- a/paddlex/deploy.py
+++ b/paddlex/deploy.py
@@ -94,7 +94,7 @@ class Predictor:
                          use_gpu=True,
                          gpu_id=0,
                          use_mkl=False,
-                         mkl_thread_num=psutil.cpu_count(),
+                         mkl_thread_num=mp.cpu_count(),
                          use_trt=False,
                          use_glog=False,
                          memory_optimize=True):
diff --git a/tutorials/train/object_detection/ppyolo.py b/tutorials/train/object_detection/ppyolo.py
index 63b47a95671692e89761251e9a1059cac9b542eb..7f1d4e32dd055851babd6eed5f823d4ea9c637e1 100644
--- a/tutorials/train/object_detection/ppyolo.py
+++ b/tutorials/train/object_detection/ppyolo.py
@@ -52,7 +52,7 @@ model.train(
     train_dataset=train_dataset,
     train_batch_size=8,
     eval_dataset=eval_dataset,
-    learning_rate=0.000125,
+    learning_rate=0.0005,
     lr_decay_epochs=[210, 240],
     save_dir='output/ppyolo',
     use_vdl=True)